The European Digital Mathematical Library: An Overview of ... ·...
55
The European Digital Mathematical Library: An Overview of Math Specific Technologies Petr Sojka Masaryk University, Faculty of Informatics, Brno, Czech Republic <[email protected]> National Institute of Informatics, Tokyo June 24th, 2013, 1:30PM
Transcript of The European Digital Mathematical Library: An Overview of ... ·...
The European Digital Mathematical LibraryAn Overview of Math Specific Technologies
Petr Sojka
Masaryk University Faculty of Informatics Brno Czech Republicltsojkafimuniczgt
National Institute of Informatics TokyoJune 24th 2013 130PM
Final identity with strapline (stacked)
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Outline and take-home message
1 Pictorial overview
2 Motivation vision of WDML PubMed Central for Mathematics
3 Data aggregation from local DMLs
4 Conversions
5 Search
6 Similarity
7 Conclusions
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Towards the dream of math-aware WDML EuDML
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Information overload in globalized scientific world
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Mathematics should follow other sciences (HEP PMChellip)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
The European Digital Mathematics Library EuDML
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
lsquoBottom uprsquo deployment towards EU or worldwide scale
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
EuDML from local data collections to the virtual DL
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
From paper to digital workflow
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Retro-digitization accessible digital library development
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Experiences from project DML-CZ for EuDML (Brno CZ)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
EuDML new approaches to math document retrieval
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
New approaches to math-aware similarity clustering andaccessibility
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Tools for automated math extraction from PDF
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Yes you can lthttpeudmlorggt accessible math searchvisibility scalabilityhellip
TEX
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
End of talk overview
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
History of the dream vision of WDML as PubMed 4 Math
In the beginning was vision of all mathematical knowledge peer reviewedverified (100000000 pages) and engineered into one-stop e-shopDL
AMS supported NSF preparation grant (in 2003) for WDMLmdashWorldwidedigital mathematics library planned to be funded by de Moore foundation($100000000 requested) Application was not successful
Publishers started massive digitization themselves
Even other attempts on the European level (FP5 FP6) were not successful
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Vision of European Digital Mathematics Library
Finally three year project or European Digital Mathematics Library EuDML(programme EU CIP-ICT-PSP type Pilot B EU contribution (16 MEur 50of total budget only) February 2010ndashJanuary 2013 The strategy of
Final identity with strapline (stacked)
was
bull to master the technology develop tools and offer them
bull concept of moving wall to motivate and engage commercial publisherswithout Open Access bussiness model
bull to collect data (from existing local or publisherrsquos) digital libraries intolsquoone-stop shoprsquo and achieve critical mass in the domainrarr lsquoa mustmetoorsquo effect then as with PubMed Central
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
EuDML as a virtual library portal
EuDML provides a virtual library based on data from smaller data providersDLs and publishers
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
One portal European Digital Mathematics Library
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Aggregation of data from building bricks of regional repositories
14 data and technology providers plus associated partners as ZMathGoumlttingen libraryhellip
DML content providers serve mostly publisherrsquos or regional more or lessestablished DML repositories The Czech Digital Mathematics LibraryDML-CZ NUMDAM DML-PL DML-PT DML-GR DML-BG DML-EShellip
Aggregation via standard OAI-PMH protocol (OAI servers run by dataproviders)
EuDML metadata schema(s) was borrowed from NLM (heavily funded by USNiH) as it allows also math-awareness (eg math stored both in TEX andMathML) and fully fledged reference lists
Inovation rather than research Example of DML-CZ lthttpdmlczgt with30000+ papers (300000+ pages) For more see (who what browsebrowse similar how to search)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Challenges of Math handling OCR indexing searchhellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Take care ldquoGod is in the detailsrdquo (Mies van der Rohe)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Data heterogenity specificity no free lunch to unify
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Document accessibility 4 DML processing challenges
Conversions (inversion of authoring+typesetting) needed from
born-digital period typesetting by TEX with export of [meta]data into digitallibrary maxTract
retro-digital period scanning geometrical transformations (BookRestorer)OCR (FineReader InftyReader) two-layer PDF
retro-born-digital period not complete tex or dvi data bad formats bitmapfonts of low resolution finally Tesseract
andDocument
Metaminusdata
RETROminusBORN DIGITAL
Image
Extraction
OCR
DataPrinted Analysis
Conversion
RETROminusDIGITISED
BORN DIGITAL
Refinement
Printed
PDFPS
TeX
Image CaptureDocument
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
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Search
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Conclusions
From PDF to MathML (via LATEX)
Most fulltext available as PDF only often as low quality scanned volumepages Aggregation via IP protected OAI-PMH including the PDFs behingmoving wall
Workflow based in the case of
born-digital PDFs on maxTract otherwise on PDFBox (plain text)
bitmap PDFs on Infty otherwise on Tesseract (no math)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
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Conclusions
Infty from Fukuoka
Run in parallel in Brno Grenoble and Lisbon to speed up Almost 200Kpapers (more than 1M pages and still running)
Working with prof Suzuki to improve further (automation support forRussian LATEX driverhellip)
Automated only no time (and money) to fix OCR errors
MathML output used for [internal] indexing and similarity computations onlynot for metadata or export
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
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Conclusions
maxTract from Birmingham II adding accessibility
Adding accessibility to mathematical documents on multiple levels
bull access to content for print impaired users such as those with visualimpairments dyslexia or dyspraxia
bull output compatible with web browsers screen readers and tools such ascopy and paste which is achieved by enriching the regular text withmathematical markup The output can also be used directly within thelimits of the presentation MathML produced as machine readablemathematical input to software systems such as Mathematica or Maple
On EuDML 10k+ fulltexts are served mostly for reading in Chrome (HTML5output) andor Adobe Acrobat Reader (as multiple-layer PDFs [no taggedPDFs yet])
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
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Conclusions
Metadata and conversions MathML and LATEX
Data heterogenity plethora of formats validation and conversions
world of authors LATEX TEX notation of mathematics
world of applicationsdata exchange XML MathML
REPOX engine (by IST Lisbon) to remap different metadata formats to uniquerepresentation
Metadata on the webmdashW3C standards MathML WAI-ARIA (WebAccessibility InitiativemdashAcessible Rich Internet Applications) WCAG (WebContent Accessibility Guidelines) 20
Big volumes rarr high automation to save costs converting to MathML (viaTralics) to allow discoverability and indexing (formulae similarity search)130+K fulltexts with MathML Infty still runninghellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Search
Vast amounts of [moving] contents in digital libraries from browsing tosearch from static links to indirect search links or even semantic search
Searching is crucial part of accessibility and exploration of the great ideasaround carved into 0s and 1s
Pragmatic decisions on math indexing level presentation vs content vssemantic In EuDML first step scalable presentation (structural) withmethods (tree indexing and weighting) extendable for content or semantic
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Math Search (MIR)
A picture is worth thousands words
ldquoA math formulae is worth of hundreds of wordsldquo (Ross Moore)
There are papers with more formulae than plain text
Precision vs Recall optimisation optimizing recall is better for exploratorysearching (we have not opted for precision as holy grail at the moment)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Motivation for MSE (including formulae) ndash cont
prof James Davenport CEIC member MKM2011 PC chair on panel atEuDML workshop in Bertinoro as a reply to the question ldquowhat functionalityand incentives would made a working mathematician to login and use amodern DML as EuDMLrdquo
ldquoMath formulae searchrdquo
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why math search is more relevant now than ever
bull Allowing formulas in queries helps to disambiguate and narrow searchSometimes the only difference among set of notionskey words would bein a math formula
bull Example 1 knowing the solution of partial differential equation in L1(C3)is there one in L2(C5)
bull Example 2 historians may want to follow the history of a (class of) formula(s)across languages and vocabularies (eg same objects studiedused byphysicists and mathematicians under different names)
bull Imagine your favourite ebook math textbook being [TEX]-search awaremdashegyour search app supports math formulae search
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
We did not start from scratch
Compare googlecomsearchq=Einstein with math-awaresearch of Einstein+$E=mcˆ2$ over arXiv
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
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Conclusions
Existing systems ndash pros and cons
bull MathDex formerly MathFind seven digit figure NSF grant by Design Science(Robert Miner) Lucene based indexing n-grams of presentation MathML pioneering conversion effort
bull EgoMath and EgoMath2 based on full text web search system Egothor presentation MathML for indexing idea of formulae augmentation α-equivalencealgorithms and relevance calculation
bull LATEXSearch MSE offered by Springer closed source only for LATEX math stringapproximate match based on strings no formulae structure matching smalldatabase 3 million formulae from lsquorandomrsquo sources
bull LeActiveMath indexing string tokens from OMDoc with OpenMath semanticnotation only for documents authored for LeActiveMath learning environment
bull DLMF only for documents authored for DLMF in special markup equation search
bull MathWeb Search semantic approach ndash uses substitution trees ndash not based on full
text searching supports Content MathML and OpenMath problem with acquiring
semantic data
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MIaS mdash Math Indexer and Searcher
bull math-aware full-text based search engine
bull joins textual and mathematical querying
bull MathML or TEX input
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MSE overall design
input document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
math
searching
indexing
Lucene Core
canonicalization
mat
h
Preprocessing into
canonicalized presentation
MathML
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Math indexing design
input canonicalized
document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
mat
h
math
searching
indexing
Lucene
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization canonicalization
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Example
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization
searchingindexing
x y+y3
x y+y3 x y y3 x y 3+
x y+y3 x y y3 x y 3+ id1id 2+id 2
3 id 1id 2 id 1
3
x y+y3 x y y3 x y 3+ id1id 2+id 2
3
id1id 2 id1
3 x y+ yconst yconst id 1id 2+id 2
const id 1const
x y+y3
x y+y2
x y+y2
x y+y 2 id 1id 2+id 2
2
x y+y 2 id1id 2+id 2
2
x y+yconst id 1id 2+id 2
const
x y+yconst id 1id 2+id 2
const Match
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formula processing example ndash subformulae weighting
(a+b2+c 0125)
(a+bc+2 0125)
(a 00875) (+ 00875) (bc+2 00875)
(b 006125) (c+2 006125)
(c 0042875)(+ 0042875)
(2 0042875)
(id 1+2 00343)
(c+const 0030625)
(id 1+const 001715)
(id 1id 2+2 007)
(bc+const 004375)
(id 1id 2+const 0035)
(id 1+id 2id 3+2 01)
(a+bc+const 00625)
(id 1+id 2id 3+const 005)
input
ordering
tokenization
variablesunification
constantsunification
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Implementation
bull Java
bull Lucene 310 now switching to LuceneSolr 4
bull Mathematical part implements Lucenersquos interface Tokenizer ndash able tointegrate to any Lucene based system
bull MIaS4Solr plugin was created for the use in Solr
bull Textual content ndash processed by StandardAnalyzer
bull easily deployable in JavaLucene based system or as a web service
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Search demonstration
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formulae search demonstration comments
Demo web interface httpaurafimunicz8085webmias
bull MathMLTEX input (Tralics [2] for conversion to MathML [])
bull Canonicalization of the query ndash problems with UMCL library [1]
bull Matched document snippet generation
bull MathJax for nicer math rendering and better portability
bull Snuggle TeX for on-the-fly as-you-type rendering
All up and ready on the EuDML system
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Searching (semantically) similar papers
Exploration of a DML browsing (semantically) similar papers
Semantic search via topic modeling Latent Semantic Indexing LatentDirichlet Allocation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Leading Edge Example Automated Meaning Picking from Texts
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Probabilistic Topical Modeling Latent Dirichlet Allocation
bull topic weighted list of words
bull document weighted list of topics
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Outline and take-home message
1 Pictorial overview
2 Motivation vision of WDML PubMed Central for Mathematics
3 Data aggregation from local DMLs
4 Conversions
5 Search
6 Similarity
7 Conclusions
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Towards the dream of math-aware WDML EuDML
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Information overload in globalized scientific world
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Mathematics should follow other sciences (HEP PMChellip)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
The European Digital Mathematics Library EuDML
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
lsquoBottom uprsquo deployment towards EU or worldwide scale
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
EuDML from local data collections to the virtual DL
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
From paper to digital workflow
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Retro-digitization accessible digital library development
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Experiences from project DML-CZ for EuDML (Brno CZ)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
EuDML new approaches to math document retrieval
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
New approaches to math-aware similarity clustering andaccessibility
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Tools for automated math extraction from PDF
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Yes you can lthttpeudmlorggt accessible math searchvisibility scalabilityhellip
TEX
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
End of talk overview
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
History of the dream vision of WDML as PubMed 4 Math
In the beginning was vision of all mathematical knowledge peer reviewedverified (100000000 pages) and engineered into one-stop e-shopDL
AMS supported NSF preparation grant (in 2003) for WDMLmdashWorldwidedigital mathematics library planned to be funded by de Moore foundation($100000000 requested) Application was not successful
Publishers started massive digitization themselves
Even other attempts on the European level (FP5 FP6) were not successful
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Vision of European Digital Mathematics Library
Finally three year project or European Digital Mathematics Library EuDML(programme EU CIP-ICT-PSP type Pilot B EU contribution (16 MEur 50of total budget only) February 2010ndashJanuary 2013 The strategy of
Final identity with strapline (stacked)
was
bull to master the technology develop tools and offer them
bull concept of moving wall to motivate and engage commercial publisherswithout Open Access bussiness model
bull to collect data (from existing local or publisherrsquos) digital libraries intolsquoone-stop shoprsquo and achieve critical mass in the domainrarr lsquoa mustmetoorsquo effect then as with PubMed Central
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
EuDML as a virtual library portal
EuDML provides a virtual library based on data from smaller data providersDLs and publishers
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
One portal European Digital Mathematics Library
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Aggregation of data from building bricks of regional repositories
14 data and technology providers plus associated partners as ZMathGoumlttingen libraryhellip
DML content providers serve mostly publisherrsquos or regional more or lessestablished DML repositories The Czech Digital Mathematics LibraryDML-CZ NUMDAM DML-PL DML-PT DML-GR DML-BG DML-EShellip
Aggregation via standard OAI-PMH protocol (OAI servers run by dataproviders)
EuDML metadata schema(s) was borrowed from NLM (heavily funded by USNiH) as it allows also math-awareness (eg math stored both in TEX andMathML) and fully fledged reference lists
Inovation rather than research Example of DML-CZ lthttpdmlczgt with30000+ papers (300000+ pages) For more see (who what browsebrowse similar how to search)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Challenges of Math handling OCR indexing searchhellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Take care ldquoGod is in the detailsrdquo (Mies van der Rohe)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Data heterogenity specificity no free lunch to unify
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Document accessibility 4 DML processing challenges
Conversions (inversion of authoring+typesetting) needed from
born-digital period typesetting by TEX with export of [meta]data into digitallibrary maxTract
retro-digital period scanning geometrical transformations (BookRestorer)OCR (FineReader InftyReader) two-layer PDF
retro-born-digital period not complete tex or dvi data bad formats bitmapfonts of low resolution finally Tesseract
andDocument
Metaminusdata
RETROminusBORN DIGITAL
Image
Extraction
OCR
DataPrinted Analysis
Conversion
RETROminusDIGITISED
BORN DIGITAL
Refinement
Printed
PDFPS
TeX
Image CaptureDocument
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
From PDF to MathML (via LATEX)
Most fulltext available as PDF only often as low quality scanned volumepages Aggregation via IP protected OAI-PMH including the PDFs behingmoving wall
Workflow based in the case of
born-digital PDFs on maxTract otherwise on PDFBox (plain text)
bitmap PDFs on Infty otherwise on Tesseract (no math)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Infty from Fukuoka
Run in parallel in Brno Grenoble and Lisbon to speed up Almost 200Kpapers (more than 1M pages and still running)
Working with prof Suzuki to improve further (automation support forRussian LATEX driverhellip)
Automated only no time (and money) to fix OCR errors
MathML output used for [internal] indexing and similarity computations onlynot for metadata or export
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham II adding accessibility
Adding accessibility to mathematical documents on multiple levels
bull access to content for print impaired users such as those with visualimpairments dyslexia or dyspraxia
bull output compatible with web browsers screen readers and tools such ascopy and paste which is achieved by enriching the regular text withmathematical markup The output can also be used directly within thelimits of the presentation MathML produced as machine readablemathematical input to software systems such as Mathematica or Maple
On EuDML 10k+ fulltexts are served mostly for reading in Chrome (HTML5output) andor Adobe Acrobat Reader (as multiple-layer PDFs [no taggedPDFs yet])
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Metadata and conversions MathML and LATEX
Data heterogenity plethora of formats validation and conversions
world of authors LATEX TEX notation of mathematics
world of applicationsdata exchange XML MathML
REPOX engine (by IST Lisbon) to remap different metadata formats to uniquerepresentation
Metadata on the webmdashW3C standards MathML WAI-ARIA (WebAccessibility InitiativemdashAcessible Rich Internet Applications) WCAG (WebContent Accessibility Guidelines) 20
Big volumes rarr high automation to save costs converting to MathML (viaTralics) to allow discoverability and indexing (formulae similarity search)130+K fulltexts with MathML Infty still runninghellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Search
Vast amounts of [moving] contents in digital libraries from browsing tosearch from static links to indirect search links or even semantic search
Searching is crucial part of accessibility and exploration of the great ideasaround carved into 0s and 1s
Pragmatic decisions on math indexing level presentation vs content vssemantic In EuDML first step scalable presentation (structural) withmethods (tree indexing and weighting) extendable for content or semantic
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Math Search (MIR)
A picture is worth thousands words
ldquoA math formulae is worth of hundreds of wordsldquo (Ross Moore)
There are papers with more formulae than plain text
Precision vs Recall optimisation optimizing recall is better for exploratorysearching (we have not opted for precision as holy grail at the moment)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Motivation for MSE (including formulae) ndash cont
prof James Davenport CEIC member MKM2011 PC chair on panel atEuDML workshop in Bertinoro as a reply to the question ldquowhat functionalityand incentives would made a working mathematician to login and use amodern DML as EuDMLrdquo
ldquoMath formulae searchrdquo
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why math search is more relevant now than ever
bull Allowing formulas in queries helps to disambiguate and narrow searchSometimes the only difference among set of notionskey words would bein a math formula
bull Example 1 knowing the solution of partial differential equation in L1(C3)is there one in L2(C5)
bull Example 2 historians may want to follow the history of a (class of) formula(s)across languages and vocabularies (eg same objects studiedused byphysicists and mathematicians under different names)
bull Imagine your favourite ebook math textbook being [TEX]-search awaremdashegyour search app supports math formulae search
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
We did not start from scratch
Compare googlecomsearchq=Einstein with math-awaresearch of Einstein+$E=mcˆ2$ over arXiv
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Existing systems ndash pros and cons
bull MathDex formerly MathFind seven digit figure NSF grant by Design Science(Robert Miner) Lucene based indexing n-grams of presentation MathML pioneering conversion effort
bull EgoMath and EgoMath2 based on full text web search system Egothor presentation MathML for indexing idea of formulae augmentation α-equivalencealgorithms and relevance calculation
bull LATEXSearch MSE offered by Springer closed source only for LATEX math stringapproximate match based on strings no formulae structure matching smalldatabase 3 million formulae from lsquorandomrsquo sources
bull LeActiveMath indexing string tokens from OMDoc with OpenMath semanticnotation only for documents authored for LeActiveMath learning environment
bull DLMF only for documents authored for DLMF in special markup equation search
bull MathWeb Search semantic approach ndash uses substitution trees ndash not based on full
text searching supports Content MathML and OpenMath problem with acquiring
semantic data
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MIaS mdash Math Indexer and Searcher
bull math-aware full-text based search engine
bull joins textual and mathematical querying
bull MathML or TEX input
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MSE overall design
input document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
math
searching
indexing
Lucene Core
canonicalization
mat
h
Preprocessing into
canonicalized presentation
MathML
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Math indexing design
input canonicalized
document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
mat
h
math
searching
indexing
Lucene
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization canonicalization
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Example
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization
searchingindexing
x y+y3
x y+y3 x y y3 x y 3+
x y+y3 x y y3 x y 3+ id1id 2+id 2
3 id 1id 2 id 1
3
x y+y3 x y y3 x y 3+ id1id 2+id 2
3
id1id 2 id1
3 x y+ yconst yconst id 1id 2+id 2
const id 1const
x y+y3
x y+y2
x y+y2
x y+y 2 id 1id 2+id 2
2
x y+y 2 id1id 2+id 2
2
x y+yconst id 1id 2+id 2
const
x y+yconst id 1id 2+id 2
const Match
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formula processing example ndash subformulae weighting
(a+b2+c 0125)
(a+bc+2 0125)
(a 00875) (+ 00875) (bc+2 00875)
(b 006125) (c+2 006125)
(c 0042875)(+ 0042875)
(2 0042875)
(id 1+2 00343)
(c+const 0030625)
(id 1+const 001715)
(id 1id 2+2 007)
(bc+const 004375)
(id 1id 2+const 0035)
(id 1+id 2id 3+2 01)
(a+bc+const 00625)
(id 1+id 2id 3+const 005)
input
ordering
tokenization
variablesunification
constantsunification
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Implementation
bull Java
bull Lucene 310 now switching to LuceneSolr 4
bull Mathematical part implements Lucenersquos interface Tokenizer ndash able tointegrate to any Lucene based system
bull MIaS4Solr plugin was created for the use in Solr
bull Textual content ndash processed by StandardAnalyzer
bull easily deployable in JavaLucene based system or as a web service
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Search demonstration
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formulae search demonstration comments
Demo web interface httpaurafimunicz8085webmias
bull MathMLTEX input (Tralics [2] for conversion to MathML [])
bull Canonicalization of the query ndash problems with UMCL library [1]
bull Matched document snippet generation
bull MathJax for nicer math rendering and better portability
bull Snuggle TeX for on-the-fly as-you-type rendering
All up and ready on the EuDML system
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Searching (semantically) similar papers
Exploration of a DML browsing (semantically) similar papers
Semantic search via topic modeling Latent Semantic Indexing LatentDirichlet Allocation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Leading Edge Example Automated Meaning Picking from Texts
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Probabilistic Topical Modeling Latent Dirichlet Allocation
bull topic weighted list of words
bull document weighted list of topics
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Towards the dream of math-aware WDML EuDML
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Information overload in globalized scientific world
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Mathematics should follow other sciences (HEP PMChellip)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
The European Digital Mathematics Library EuDML
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
lsquoBottom uprsquo deployment towards EU or worldwide scale
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
EuDML from local data collections to the virtual DL
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
From paper to digital workflow
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Retro-digitization accessible digital library development
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Experiences from project DML-CZ for EuDML (Brno CZ)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
EuDML new approaches to math document retrieval
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
New approaches to math-aware similarity clustering andaccessibility
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Tools for automated math extraction from PDF
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Yes you can lthttpeudmlorggt accessible math searchvisibility scalabilityhellip
TEX
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
End of talk overview
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
History of the dream vision of WDML as PubMed 4 Math
In the beginning was vision of all mathematical knowledge peer reviewedverified (100000000 pages) and engineered into one-stop e-shopDL
AMS supported NSF preparation grant (in 2003) for WDMLmdashWorldwidedigital mathematics library planned to be funded by de Moore foundation($100000000 requested) Application was not successful
Publishers started massive digitization themselves
Even other attempts on the European level (FP5 FP6) were not successful
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Vision of European Digital Mathematics Library
Finally three year project or European Digital Mathematics Library EuDML(programme EU CIP-ICT-PSP type Pilot B EU contribution (16 MEur 50of total budget only) February 2010ndashJanuary 2013 The strategy of
Final identity with strapline (stacked)
was
bull to master the technology develop tools and offer them
bull concept of moving wall to motivate and engage commercial publisherswithout Open Access bussiness model
bull to collect data (from existing local or publisherrsquos) digital libraries intolsquoone-stop shoprsquo and achieve critical mass in the domainrarr lsquoa mustmetoorsquo effect then as with PubMed Central
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
EuDML as a virtual library portal
EuDML provides a virtual library based on data from smaller data providersDLs and publishers
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
One portal European Digital Mathematics Library
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Aggregation of data from building bricks of regional repositories
14 data and technology providers plus associated partners as ZMathGoumlttingen libraryhellip
DML content providers serve mostly publisherrsquos or regional more or lessestablished DML repositories The Czech Digital Mathematics LibraryDML-CZ NUMDAM DML-PL DML-PT DML-GR DML-BG DML-EShellip
Aggregation via standard OAI-PMH protocol (OAI servers run by dataproviders)
EuDML metadata schema(s) was borrowed from NLM (heavily funded by USNiH) as it allows also math-awareness (eg math stored both in TEX andMathML) and fully fledged reference lists
Inovation rather than research Example of DML-CZ lthttpdmlczgt with30000+ papers (300000+ pages) For more see (who what browsebrowse similar how to search)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Challenges of Math handling OCR indexing searchhellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Take care ldquoGod is in the detailsrdquo (Mies van der Rohe)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Data heterogenity specificity no free lunch to unify
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Document accessibility 4 DML processing challenges
Conversions (inversion of authoring+typesetting) needed from
born-digital period typesetting by TEX with export of [meta]data into digitallibrary maxTract
retro-digital period scanning geometrical transformations (BookRestorer)OCR (FineReader InftyReader) two-layer PDF
retro-born-digital period not complete tex or dvi data bad formats bitmapfonts of low resolution finally Tesseract
andDocument
Metaminusdata
RETROminusBORN DIGITAL
Image
Extraction
OCR
DataPrinted Analysis
Conversion
RETROminusDIGITISED
BORN DIGITAL
Refinement
Printed
PDFPS
TeX
Image CaptureDocument
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
From PDF to MathML (via LATEX)
Most fulltext available as PDF only often as low quality scanned volumepages Aggregation via IP protected OAI-PMH including the PDFs behingmoving wall
Workflow based in the case of
born-digital PDFs on maxTract otherwise on PDFBox (plain text)
bitmap PDFs on Infty otherwise on Tesseract (no math)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Infty from Fukuoka
Run in parallel in Brno Grenoble and Lisbon to speed up Almost 200Kpapers (more than 1M pages and still running)
Working with prof Suzuki to improve further (automation support forRussian LATEX driverhellip)
Automated only no time (and money) to fix OCR errors
MathML output used for [internal] indexing and similarity computations onlynot for metadata or export
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham II adding accessibility
Adding accessibility to mathematical documents on multiple levels
bull access to content for print impaired users such as those with visualimpairments dyslexia or dyspraxia
bull output compatible with web browsers screen readers and tools such ascopy and paste which is achieved by enriching the regular text withmathematical markup The output can also be used directly within thelimits of the presentation MathML produced as machine readablemathematical input to software systems such as Mathematica or Maple
On EuDML 10k+ fulltexts are served mostly for reading in Chrome (HTML5output) andor Adobe Acrobat Reader (as multiple-layer PDFs [no taggedPDFs yet])
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Metadata and conversions MathML and LATEX
Data heterogenity plethora of formats validation and conversions
world of authors LATEX TEX notation of mathematics
world of applicationsdata exchange XML MathML
REPOX engine (by IST Lisbon) to remap different metadata formats to uniquerepresentation
Metadata on the webmdashW3C standards MathML WAI-ARIA (WebAccessibility InitiativemdashAcessible Rich Internet Applications) WCAG (WebContent Accessibility Guidelines) 20
Big volumes rarr high automation to save costs converting to MathML (viaTralics) to allow discoverability and indexing (formulae similarity search)130+K fulltexts with MathML Infty still runninghellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Search
Vast amounts of [moving] contents in digital libraries from browsing tosearch from static links to indirect search links or even semantic search
Searching is crucial part of accessibility and exploration of the great ideasaround carved into 0s and 1s
Pragmatic decisions on math indexing level presentation vs content vssemantic In EuDML first step scalable presentation (structural) withmethods (tree indexing and weighting) extendable for content or semantic
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Math Search (MIR)
A picture is worth thousands words
ldquoA math formulae is worth of hundreds of wordsldquo (Ross Moore)
There are papers with more formulae than plain text
Precision vs Recall optimisation optimizing recall is better for exploratorysearching (we have not opted for precision as holy grail at the moment)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Motivation for MSE (including formulae) ndash cont
prof James Davenport CEIC member MKM2011 PC chair on panel atEuDML workshop in Bertinoro as a reply to the question ldquowhat functionalityand incentives would made a working mathematician to login and use amodern DML as EuDMLrdquo
ldquoMath formulae searchrdquo
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why math search is more relevant now than ever
bull Allowing formulas in queries helps to disambiguate and narrow searchSometimes the only difference among set of notionskey words would bein a math formula
bull Example 1 knowing the solution of partial differential equation in L1(C3)is there one in L2(C5)
bull Example 2 historians may want to follow the history of a (class of) formula(s)across languages and vocabularies (eg same objects studiedused byphysicists and mathematicians under different names)
bull Imagine your favourite ebook math textbook being [TEX]-search awaremdashegyour search app supports math formulae search
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
We did not start from scratch
Compare googlecomsearchq=Einstein with math-awaresearch of Einstein+$E=mcˆ2$ over arXiv
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Existing systems ndash pros and cons
bull MathDex formerly MathFind seven digit figure NSF grant by Design Science(Robert Miner) Lucene based indexing n-grams of presentation MathML pioneering conversion effort
bull EgoMath and EgoMath2 based on full text web search system Egothor presentation MathML for indexing idea of formulae augmentation α-equivalencealgorithms and relevance calculation
bull LATEXSearch MSE offered by Springer closed source only for LATEX math stringapproximate match based on strings no formulae structure matching smalldatabase 3 million formulae from lsquorandomrsquo sources
bull LeActiveMath indexing string tokens from OMDoc with OpenMath semanticnotation only for documents authored for LeActiveMath learning environment
bull DLMF only for documents authored for DLMF in special markup equation search
bull MathWeb Search semantic approach ndash uses substitution trees ndash not based on full
text searching supports Content MathML and OpenMath problem with acquiring
semantic data
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MIaS mdash Math Indexer and Searcher
bull math-aware full-text based search engine
bull joins textual and mathematical querying
bull MathML or TEX input
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MSE overall design
input document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
math
searching
indexing
Lucene Core
canonicalization
mat
h
Preprocessing into
canonicalized presentation
MathML
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Math indexing design
input canonicalized
document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
mat
h
math
searching
indexing
Lucene
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization canonicalization
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Example
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization
searchingindexing
x y+y3
x y+y3 x y y3 x y 3+
x y+y3 x y y3 x y 3+ id1id 2+id 2
3 id 1id 2 id 1
3
x y+y3 x y y3 x y 3+ id1id 2+id 2
3
id1id 2 id1
3 x y+ yconst yconst id 1id 2+id 2
const id 1const
x y+y3
x y+y2
x y+y2
x y+y 2 id 1id 2+id 2
2
x y+y 2 id1id 2+id 2
2
x y+yconst id 1id 2+id 2
const
x y+yconst id 1id 2+id 2
const Match
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formula processing example ndash subformulae weighting
(a+b2+c 0125)
(a+bc+2 0125)
(a 00875) (+ 00875) (bc+2 00875)
(b 006125) (c+2 006125)
(c 0042875)(+ 0042875)
(2 0042875)
(id 1+2 00343)
(c+const 0030625)
(id 1+const 001715)
(id 1id 2+2 007)
(bc+const 004375)
(id 1id 2+const 0035)
(id 1+id 2id 3+2 01)
(a+bc+const 00625)
(id 1+id 2id 3+const 005)
input
ordering
tokenization
variablesunification
constantsunification
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Implementation
bull Java
bull Lucene 310 now switching to LuceneSolr 4
bull Mathematical part implements Lucenersquos interface Tokenizer ndash able tointegrate to any Lucene based system
bull MIaS4Solr plugin was created for the use in Solr
bull Textual content ndash processed by StandardAnalyzer
bull easily deployable in JavaLucene based system or as a web service
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Search demonstration
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formulae search demonstration comments
Demo web interface httpaurafimunicz8085webmias
bull MathMLTEX input (Tralics [2] for conversion to MathML [])
bull Canonicalization of the query ndash problems with UMCL library [1]
bull Matched document snippet generation
bull MathJax for nicer math rendering and better portability
bull Snuggle TeX for on-the-fly as-you-type rendering
All up and ready on the EuDML system
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Searching (semantically) similar papers
Exploration of a DML browsing (semantically) similar papers
Semantic search via topic modeling Latent Semantic Indexing LatentDirichlet Allocation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Leading Edge Example Automated Meaning Picking from Texts
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Probabilistic Topical Modeling Latent Dirichlet Allocation
bull topic weighted list of words
bull document weighted list of topics
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Information overload in globalized scientific world
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Mathematics should follow other sciences (HEP PMChellip)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
The European Digital Mathematics Library EuDML
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
lsquoBottom uprsquo deployment towards EU or worldwide scale
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
EuDML from local data collections to the virtual DL
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
From paper to digital workflow
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Retro-digitization accessible digital library development
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Experiences from project DML-CZ for EuDML (Brno CZ)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
EuDML new approaches to math document retrieval
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
New approaches to math-aware similarity clustering andaccessibility
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Tools for automated math extraction from PDF
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Yes you can lthttpeudmlorggt accessible math searchvisibility scalabilityhellip
TEX
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
End of talk overview
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
History of the dream vision of WDML as PubMed 4 Math
In the beginning was vision of all mathematical knowledge peer reviewedverified (100000000 pages) and engineered into one-stop e-shopDL
AMS supported NSF preparation grant (in 2003) for WDMLmdashWorldwidedigital mathematics library planned to be funded by de Moore foundation($100000000 requested) Application was not successful
Publishers started massive digitization themselves
Even other attempts on the European level (FP5 FP6) were not successful
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Vision of European Digital Mathematics Library
Finally three year project or European Digital Mathematics Library EuDML(programme EU CIP-ICT-PSP type Pilot B EU contribution (16 MEur 50of total budget only) February 2010ndashJanuary 2013 The strategy of
Final identity with strapline (stacked)
was
bull to master the technology develop tools and offer them
bull concept of moving wall to motivate and engage commercial publisherswithout Open Access bussiness model
bull to collect data (from existing local or publisherrsquos) digital libraries intolsquoone-stop shoprsquo and achieve critical mass in the domainrarr lsquoa mustmetoorsquo effect then as with PubMed Central
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
EuDML as a virtual library portal
EuDML provides a virtual library based on data from smaller data providersDLs and publishers
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
One portal European Digital Mathematics Library
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Aggregation of data from building bricks of regional repositories
14 data and technology providers plus associated partners as ZMathGoumlttingen libraryhellip
DML content providers serve mostly publisherrsquos or regional more or lessestablished DML repositories The Czech Digital Mathematics LibraryDML-CZ NUMDAM DML-PL DML-PT DML-GR DML-BG DML-EShellip
Aggregation via standard OAI-PMH protocol (OAI servers run by dataproviders)
EuDML metadata schema(s) was borrowed from NLM (heavily funded by USNiH) as it allows also math-awareness (eg math stored both in TEX andMathML) and fully fledged reference lists
Inovation rather than research Example of DML-CZ lthttpdmlczgt with30000+ papers (300000+ pages) For more see (who what browsebrowse similar how to search)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Challenges of Math handling OCR indexing searchhellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Take care ldquoGod is in the detailsrdquo (Mies van der Rohe)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Data heterogenity specificity no free lunch to unify
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Document accessibility 4 DML processing challenges
Conversions (inversion of authoring+typesetting) needed from
born-digital period typesetting by TEX with export of [meta]data into digitallibrary maxTract
retro-digital period scanning geometrical transformations (BookRestorer)OCR (FineReader InftyReader) two-layer PDF
retro-born-digital period not complete tex or dvi data bad formats bitmapfonts of low resolution finally Tesseract
andDocument
Metaminusdata
RETROminusBORN DIGITAL
Image
Extraction
OCR
DataPrinted Analysis
Conversion
RETROminusDIGITISED
BORN DIGITAL
Refinement
Printed
PDFPS
TeX
Image CaptureDocument
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
From PDF to MathML (via LATEX)
Most fulltext available as PDF only often as low quality scanned volumepages Aggregation via IP protected OAI-PMH including the PDFs behingmoving wall
Workflow based in the case of
born-digital PDFs on maxTract otherwise on PDFBox (plain text)
bitmap PDFs on Infty otherwise on Tesseract (no math)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Infty from Fukuoka
Run in parallel in Brno Grenoble and Lisbon to speed up Almost 200Kpapers (more than 1M pages and still running)
Working with prof Suzuki to improve further (automation support forRussian LATEX driverhellip)
Automated only no time (and money) to fix OCR errors
MathML output used for [internal] indexing and similarity computations onlynot for metadata or export
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham II adding accessibility
Adding accessibility to mathematical documents on multiple levels
bull access to content for print impaired users such as those with visualimpairments dyslexia or dyspraxia
bull output compatible with web browsers screen readers and tools such ascopy and paste which is achieved by enriching the regular text withmathematical markup The output can also be used directly within thelimits of the presentation MathML produced as machine readablemathematical input to software systems such as Mathematica or Maple
On EuDML 10k+ fulltexts are served mostly for reading in Chrome (HTML5output) andor Adobe Acrobat Reader (as multiple-layer PDFs [no taggedPDFs yet])
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Metadata and conversions MathML and LATEX
Data heterogenity plethora of formats validation and conversions
world of authors LATEX TEX notation of mathematics
world of applicationsdata exchange XML MathML
REPOX engine (by IST Lisbon) to remap different metadata formats to uniquerepresentation
Metadata on the webmdashW3C standards MathML WAI-ARIA (WebAccessibility InitiativemdashAcessible Rich Internet Applications) WCAG (WebContent Accessibility Guidelines) 20
Big volumes rarr high automation to save costs converting to MathML (viaTralics) to allow discoverability and indexing (formulae similarity search)130+K fulltexts with MathML Infty still runninghellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Search
Vast amounts of [moving] contents in digital libraries from browsing tosearch from static links to indirect search links or even semantic search
Searching is crucial part of accessibility and exploration of the great ideasaround carved into 0s and 1s
Pragmatic decisions on math indexing level presentation vs content vssemantic In EuDML first step scalable presentation (structural) withmethods (tree indexing and weighting) extendable for content or semantic
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Math Search (MIR)
A picture is worth thousands words
ldquoA math formulae is worth of hundreds of wordsldquo (Ross Moore)
There are papers with more formulae than plain text
Precision vs Recall optimisation optimizing recall is better for exploratorysearching (we have not opted for precision as holy grail at the moment)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Motivation for MSE (including formulae) ndash cont
prof James Davenport CEIC member MKM2011 PC chair on panel atEuDML workshop in Bertinoro as a reply to the question ldquowhat functionalityand incentives would made a working mathematician to login and use amodern DML as EuDMLrdquo
ldquoMath formulae searchrdquo
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why math search is more relevant now than ever
bull Allowing formulas in queries helps to disambiguate and narrow searchSometimes the only difference among set of notionskey words would bein a math formula
bull Example 1 knowing the solution of partial differential equation in L1(C3)is there one in L2(C5)
bull Example 2 historians may want to follow the history of a (class of) formula(s)across languages and vocabularies (eg same objects studiedused byphysicists and mathematicians under different names)
bull Imagine your favourite ebook math textbook being [TEX]-search awaremdashegyour search app supports math formulae search
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
We did not start from scratch
Compare googlecomsearchq=Einstein with math-awaresearch of Einstein+$E=mcˆ2$ over arXiv
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Existing systems ndash pros and cons
bull MathDex formerly MathFind seven digit figure NSF grant by Design Science(Robert Miner) Lucene based indexing n-grams of presentation MathML pioneering conversion effort
bull EgoMath and EgoMath2 based on full text web search system Egothor presentation MathML for indexing idea of formulae augmentation α-equivalencealgorithms and relevance calculation
bull LATEXSearch MSE offered by Springer closed source only for LATEX math stringapproximate match based on strings no formulae structure matching smalldatabase 3 million formulae from lsquorandomrsquo sources
bull LeActiveMath indexing string tokens from OMDoc with OpenMath semanticnotation only for documents authored for LeActiveMath learning environment
bull DLMF only for documents authored for DLMF in special markup equation search
bull MathWeb Search semantic approach ndash uses substitution trees ndash not based on full
text searching supports Content MathML and OpenMath problem with acquiring
semantic data
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MIaS mdash Math Indexer and Searcher
bull math-aware full-text based search engine
bull joins textual and mathematical querying
bull MathML or TEX input
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MSE overall design
input document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
math
searching
indexing
Lucene Core
canonicalization
mat
h
Preprocessing into
canonicalized presentation
MathML
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Math indexing design
input canonicalized
document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
mat
h
math
searching
indexing
Lucene
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization canonicalization
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Example
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization
searchingindexing
x y+y3
x y+y3 x y y3 x y 3+
x y+y3 x y y3 x y 3+ id1id 2+id 2
3 id 1id 2 id 1
3
x y+y3 x y y3 x y 3+ id1id 2+id 2
3
id1id 2 id1
3 x y+ yconst yconst id 1id 2+id 2
const id 1const
x y+y3
x y+y2
x y+y2
x y+y 2 id 1id 2+id 2
2
x y+y 2 id1id 2+id 2
2
x y+yconst id 1id 2+id 2
const
x y+yconst id 1id 2+id 2
const Match
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formula processing example ndash subformulae weighting
(a+b2+c 0125)
(a+bc+2 0125)
(a 00875) (+ 00875) (bc+2 00875)
(b 006125) (c+2 006125)
(c 0042875)(+ 0042875)
(2 0042875)
(id 1+2 00343)
(c+const 0030625)
(id 1+const 001715)
(id 1id 2+2 007)
(bc+const 004375)
(id 1id 2+const 0035)
(id 1+id 2id 3+2 01)
(a+bc+const 00625)
(id 1+id 2id 3+const 005)
input
ordering
tokenization
variablesunification
constantsunification
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Implementation
bull Java
bull Lucene 310 now switching to LuceneSolr 4
bull Mathematical part implements Lucenersquos interface Tokenizer ndash able tointegrate to any Lucene based system
bull MIaS4Solr plugin was created for the use in Solr
bull Textual content ndash processed by StandardAnalyzer
bull easily deployable in JavaLucene based system or as a web service
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Search demonstration
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formulae search demonstration comments
Demo web interface httpaurafimunicz8085webmias
bull MathMLTEX input (Tralics [2] for conversion to MathML [])
bull Canonicalization of the query ndash problems with UMCL library [1]
bull Matched document snippet generation
bull MathJax for nicer math rendering and better portability
bull Snuggle TeX for on-the-fly as-you-type rendering
All up and ready on the EuDML system
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Searching (semantically) similar papers
Exploration of a DML browsing (semantically) similar papers
Semantic search via topic modeling Latent Semantic Indexing LatentDirichlet Allocation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Leading Edge Example Automated Meaning Picking from Texts
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Probabilistic Topical Modeling Latent Dirichlet Allocation
bull topic weighted list of words
bull document weighted list of topics
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Mathematics should follow other sciences (HEP PMChellip)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
The European Digital Mathematics Library EuDML
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
lsquoBottom uprsquo deployment towards EU or worldwide scale
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
EuDML from local data collections to the virtual DL
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
From paper to digital workflow
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Retro-digitization accessible digital library development
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Experiences from project DML-CZ for EuDML (Brno CZ)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
EuDML new approaches to math document retrieval
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
New approaches to math-aware similarity clustering andaccessibility
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Tools for automated math extraction from PDF
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Yes you can lthttpeudmlorggt accessible math searchvisibility scalabilityhellip
TEX
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
End of talk overview
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
History of the dream vision of WDML as PubMed 4 Math
In the beginning was vision of all mathematical knowledge peer reviewedverified (100000000 pages) and engineered into one-stop e-shopDL
AMS supported NSF preparation grant (in 2003) for WDMLmdashWorldwidedigital mathematics library planned to be funded by de Moore foundation($100000000 requested) Application was not successful
Publishers started massive digitization themselves
Even other attempts on the European level (FP5 FP6) were not successful
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Vision of European Digital Mathematics Library
Finally three year project or European Digital Mathematics Library EuDML(programme EU CIP-ICT-PSP type Pilot B EU contribution (16 MEur 50of total budget only) February 2010ndashJanuary 2013 The strategy of
Final identity with strapline (stacked)
was
bull to master the technology develop tools and offer them
bull concept of moving wall to motivate and engage commercial publisherswithout Open Access bussiness model
bull to collect data (from existing local or publisherrsquos) digital libraries intolsquoone-stop shoprsquo and achieve critical mass in the domainrarr lsquoa mustmetoorsquo effect then as with PubMed Central
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
EuDML as a virtual library portal
EuDML provides a virtual library based on data from smaller data providersDLs and publishers
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
One portal European Digital Mathematics Library
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Aggregation of data from building bricks of regional repositories
14 data and technology providers plus associated partners as ZMathGoumlttingen libraryhellip
DML content providers serve mostly publisherrsquos or regional more or lessestablished DML repositories The Czech Digital Mathematics LibraryDML-CZ NUMDAM DML-PL DML-PT DML-GR DML-BG DML-EShellip
Aggregation via standard OAI-PMH protocol (OAI servers run by dataproviders)
EuDML metadata schema(s) was borrowed from NLM (heavily funded by USNiH) as it allows also math-awareness (eg math stored both in TEX andMathML) and fully fledged reference lists
Inovation rather than research Example of DML-CZ lthttpdmlczgt with30000+ papers (300000+ pages) For more see (who what browsebrowse similar how to search)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Challenges of Math handling OCR indexing searchhellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Take care ldquoGod is in the detailsrdquo (Mies van der Rohe)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Data heterogenity specificity no free lunch to unify
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Document accessibility 4 DML processing challenges
Conversions (inversion of authoring+typesetting) needed from
born-digital period typesetting by TEX with export of [meta]data into digitallibrary maxTract
retro-digital period scanning geometrical transformations (BookRestorer)OCR (FineReader InftyReader) two-layer PDF
retro-born-digital period not complete tex or dvi data bad formats bitmapfonts of low resolution finally Tesseract
andDocument
Metaminusdata
RETROminusBORN DIGITAL
Image
Extraction
OCR
DataPrinted Analysis
Conversion
RETROminusDIGITISED
BORN DIGITAL
Refinement
Printed
PDFPS
TeX
Image CaptureDocument
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
From PDF to MathML (via LATEX)
Most fulltext available as PDF only often as low quality scanned volumepages Aggregation via IP protected OAI-PMH including the PDFs behingmoving wall
Workflow based in the case of
born-digital PDFs on maxTract otherwise on PDFBox (plain text)
bitmap PDFs on Infty otherwise on Tesseract (no math)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Infty from Fukuoka
Run in parallel in Brno Grenoble and Lisbon to speed up Almost 200Kpapers (more than 1M pages and still running)
Working with prof Suzuki to improve further (automation support forRussian LATEX driverhellip)
Automated only no time (and money) to fix OCR errors
MathML output used for [internal] indexing and similarity computations onlynot for metadata or export
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham II adding accessibility
Adding accessibility to mathematical documents on multiple levels
bull access to content for print impaired users such as those with visualimpairments dyslexia or dyspraxia
bull output compatible with web browsers screen readers and tools such ascopy and paste which is achieved by enriching the regular text withmathematical markup The output can also be used directly within thelimits of the presentation MathML produced as machine readablemathematical input to software systems such as Mathematica or Maple
On EuDML 10k+ fulltexts are served mostly for reading in Chrome (HTML5output) andor Adobe Acrobat Reader (as multiple-layer PDFs [no taggedPDFs yet])
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Metadata and conversions MathML and LATEX
Data heterogenity plethora of formats validation and conversions
world of authors LATEX TEX notation of mathematics
world of applicationsdata exchange XML MathML
REPOX engine (by IST Lisbon) to remap different metadata formats to uniquerepresentation
Metadata on the webmdashW3C standards MathML WAI-ARIA (WebAccessibility InitiativemdashAcessible Rich Internet Applications) WCAG (WebContent Accessibility Guidelines) 20
Big volumes rarr high automation to save costs converting to MathML (viaTralics) to allow discoverability and indexing (formulae similarity search)130+K fulltexts with MathML Infty still runninghellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Search
Vast amounts of [moving] contents in digital libraries from browsing tosearch from static links to indirect search links or even semantic search
Searching is crucial part of accessibility and exploration of the great ideasaround carved into 0s and 1s
Pragmatic decisions on math indexing level presentation vs content vssemantic In EuDML first step scalable presentation (structural) withmethods (tree indexing and weighting) extendable for content or semantic
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Math Search (MIR)
A picture is worth thousands words
ldquoA math formulae is worth of hundreds of wordsldquo (Ross Moore)
There are papers with more formulae than plain text
Precision vs Recall optimisation optimizing recall is better for exploratorysearching (we have not opted for precision as holy grail at the moment)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Motivation for MSE (including formulae) ndash cont
prof James Davenport CEIC member MKM2011 PC chair on panel atEuDML workshop in Bertinoro as a reply to the question ldquowhat functionalityand incentives would made a working mathematician to login and use amodern DML as EuDMLrdquo
ldquoMath formulae searchrdquo
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why math search is more relevant now than ever
bull Allowing formulas in queries helps to disambiguate and narrow searchSometimes the only difference among set of notionskey words would bein a math formula
bull Example 1 knowing the solution of partial differential equation in L1(C3)is there one in L2(C5)
bull Example 2 historians may want to follow the history of a (class of) formula(s)across languages and vocabularies (eg same objects studiedused byphysicists and mathematicians under different names)
bull Imagine your favourite ebook math textbook being [TEX]-search awaremdashegyour search app supports math formulae search
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
We did not start from scratch
Compare googlecomsearchq=Einstein with math-awaresearch of Einstein+$E=mcˆ2$ over arXiv
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Existing systems ndash pros and cons
bull MathDex formerly MathFind seven digit figure NSF grant by Design Science(Robert Miner) Lucene based indexing n-grams of presentation MathML pioneering conversion effort
bull EgoMath and EgoMath2 based on full text web search system Egothor presentation MathML for indexing idea of formulae augmentation α-equivalencealgorithms and relevance calculation
bull LATEXSearch MSE offered by Springer closed source only for LATEX math stringapproximate match based on strings no formulae structure matching smalldatabase 3 million formulae from lsquorandomrsquo sources
bull LeActiveMath indexing string tokens from OMDoc with OpenMath semanticnotation only for documents authored for LeActiveMath learning environment
bull DLMF only for documents authored for DLMF in special markup equation search
bull MathWeb Search semantic approach ndash uses substitution trees ndash not based on full
text searching supports Content MathML and OpenMath problem with acquiring
semantic data
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MIaS mdash Math Indexer and Searcher
bull math-aware full-text based search engine
bull joins textual and mathematical querying
bull MathML or TEX input
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MSE overall design
input document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
math
searching
indexing
Lucene Core
canonicalization
mat
h
Preprocessing into
canonicalized presentation
MathML
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Math indexing design
input canonicalized
document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
mat
h
math
searching
indexing
Lucene
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization canonicalization
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Example
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization
searchingindexing
x y+y3
x y+y3 x y y3 x y 3+
x y+y3 x y y3 x y 3+ id1id 2+id 2
3 id 1id 2 id 1
3
x y+y3 x y y3 x y 3+ id1id 2+id 2
3
id1id 2 id1
3 x y+ yconst yconst id 1id 2+id 2
const id 1const
x y+y3
x y+y2
x y+y2
x y+y 2 id 1id 2+id 2
2
x y+y 2 id1id 2+id 2
2
x y+yconst id 1id 2+id 2
const
x y+yconst id 1id 2+id 2
const Match
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formula processing example ndash subformulae weighting
(a+b2+c 0125)
(a+bc+2 0125)
(a 00875) (+ 00875) (bc+2 00875)
(b 006125) (c+2 006125)
(c 0042875)(+ 0042875)
(2 0042875)
(id 1+2 00343)
(c+const 0030625)
(id 1+const 001715)
(id 1id 2+2 007)
(bc+const 004375)
(id 1id 2+const 0035)
(id 1+id 2id 3+2 01)
(a+bc+const 00625)
(id 1+id 2id 3+const 005)
input
ordering
tokenization
variablesunification
constantsunification
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Implementation
bull Java
bull Lucene 310 now switching to LuceneSolr 4
bull Mathematical part implements Lucenersquos interface Tokenizer ndash able tointegrate to any Lucene based system
bull MIaS4Solr plugin was created for the use in Solr
bull Textual content ndash processed by StandardAnalyzer
bull easily deployable in JavaLucene based system or as a web service
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Search demonstration
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formulae search demonstration comments
Demo web interface httpaurafimunicz8085webmias
bull MathMLTEX input (Tralics [2] for conversion to MathML [])
bull Canonicalization of the query ndash problems with UMCL library [1]
bull Matched document snippet generation
bull MathJax for nicer math rendering and better portability
bull Snuggle TeX for on-the-fly as-you-type rendering
All up and ready on the EuDML system
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Searching (semantically) similar papers
Exploration of a DML browsing (semantically) similar papers
Semantic search via topic modeling Latent Semantic Indexing LatentDirichlet Allocation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Leading Edge Example Automated Meaning Picking from Texts
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Probabilistic Topical Modeling Latent Dirichlet Allocation
bull topic weighted list of words
bull document weighted list of topics
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
The European Digital Mathematics Library EuDML
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
lsquoBottom uprsquo deployment towards EU or worldwide scale
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
EuDML from local data collections to the virtual DL
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
From paper to digital workflow
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Retro-digitization accessible digital library development
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Experiences from project DML-CZ for EuDML (Brno CZ)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
EuDML new approaches to math document retrieval
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
New approaches to math-aware similarity clustering andaccessibility
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Tools for automated math extraction from PDF
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Yes you can lthttpeudmlorggt accessible math searchvisibility scalabilityhellip
TEX
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
End of talk overview
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
History of the dream vision of WDML as PubMed 4 Math
In the beginning was vision of all mathematical knowledge peer reviewedverified (100000000 pages) and engineered into one-stop e-shopDL
AMS supported NSF preparation grant (in 2003) for WDMLmdashWorldwidedigital mathematics library planned to be funded by de Moore foundation($100000000 requested) Application was not successful
Publishers started massive digitization themselves
Even other attempts on the European level (FP5 FP6) were not successful
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Vision of European Digital Mathematics Library
Finally three year project or European Digital Mathematics Library EuDML(programme EU CIP-ICT-PSP type Pilot B EU contribution (16 MEur 50of total budget only) February 2010ndashJanuary 2013 The strategy of
Final identity with strapline (stacked)
was
bull to master the technology develop tools and offer them
bull concept of moving wall to motivate and engage commercial publisherswithout Open Access bussiness model
bull to collect data (from existing local or publisherrsquos) digital libraries intolsquoone-stop shoprsquo and achieve critical mass in the domainrarr lsquoa mustmetoorsquo effect then as with PubMed Central
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
EuDML as a virtual library portal
EuDML provides a virtual library based on data from smaller data providersDLs and publishers
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
One portal European Digital Mathematics Library
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Aggregation of data from building bricks of regional repositories
14 data and technology providers plus associated partners as ZMathGoumlttingen libraryhellip
DML content providers serve mostly publisherrsquos or regional more or lessestablished DML repositories The Czech Digital Mathematics LibraryDML-CZ NUMDAM DML-PL DML-PT DML-GR DML-BG DML-EShellip
Aggregation via standard OAI-PMH protocol (OAI servers run by dataproviders)
EuDML metadata schema(s) was borrowed from NLM (heavily funded by USNiH) as it allows also math-awareness (eg math stored both in TEX andMathML) and fully fledged reference lists
Inovation rather than research Example of DML-CZ lthttpdmlczgt with30000+ papers (300000+ pages) For more see (who what browsebrowse similar how to search)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Challenges of Math handling OCR indexing searchhellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Take care ldquoGod is in the detailsrdquo (Mies van der Rohe)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Data heterogenity specificity no free lunch to unify
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Document accessibility 4 DML processing challenges
Conversions (inversion of authoring+typesetting) needed from
born-digital period typesetting by TEX with export of [meta]data into digitallibrary maxTract
retro-digital period scanning geometrical transformations (BookRestorer)OCR (FineReader InftyReader) two-layer PDF
retro-born-digital period not complete tex or dvi data bad formats bitmapfonts of low resolution finally Tesseract
andDocument
Metaminusdata
RETROminusBORN DIGITAL
Image
Extraction
OCR
DataPrinted Analysis
Conversion
RETROminusDIGITISED
BORN DIGITAL
Refinement
Printed
PDFPS
TeX
Image CaptureDocument
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
From PDF to MathML (via LATEX)
Most fulltext available as PDF only often as low quality scanned volumepages Aggregation via IP protected OAI-PMH including the PDFs behingmoving wall
Workflow based in the case of
born-digital PDFs on maxTract otherwise on PDFBox (plain text)
bitmap PDFs on Infty otherwise on Tesseract (no math)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Infty from Fukuoka
Run in parallel in Brno Grenoble and Lisbon to speed up Almost 200Kpapers (more than 1M pages and still running)
Working with prof Suzuki to improve further (automation support forRussian LATEX driverhellip)
Automated only no time (and money) to fix OCR errors
MathML output used for [internal] indexing and similarity computations onlynot for metadata or export
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham II adding accessibility
Adding accessibility to mathematical documents on multiple levels
bull access to content for print impaired users such as those with visualimpairments dyslexia or dyspraxia
bull output compatible with web browsers screen readers and tools such ascopy and paste which is achieved by enriching the regular text withmathematical markup The output can also be used directly within thelimits of the presentation MathML produced as machine readablemathematical input to software systems such as Mathematica or Maple
On EuDML 10k+ fulltexts are served mostly for reading in Chrome (HTML5output) andor Adobe Acrobat Reader (as multiple-layer PDFs [no taggedPDFs yet])
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Metadata and conversions MathML and LATEX
Data heterogenity plethora of formats validation and conversions
world of authors LATEX TEX notation of mathematics
world of applicationsdata exchange XML MathML
REPOX engine (by IST Lisbon) to remap different metadata formats to uniquerepresentation
Metadata on the webmdashW3C standards MathML WAI-ARIA (WebAccessibility InitiativemdashAcessible Rich Internet Applications) WCAG (WebContent Accessibility Guidelines) 20
Big volumes rarr high automation to save costs converting to MathML (viaTralics) to allow discoverability and indexing (formulae similarity search)130+K fulltexts with MathML Infty still runninghellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Search
Vast amounts of [moving] contents in digital libraries from browsing tosearch from static links to indirect search links or even semantic search
Searching is crucial part of accessibility and exploration of the great ideasaround carved into 0s and 1s
Pragmatic decisions on math indexing level presentation vs content vssemantic In EuDML first step scalable presentation (structural) withmethods (tree indexing and weighting) extendable for content or semantic
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Math Search (MIR)
A picture is worth thousands words
ldquoA math formulae is worth of hundreds of wordsldquo (Ross Moore)
There are papers with more formulae than plain text
Precision vs Recall optimisation optimizing recall is better for exploratorysearching (we have not opted for precision as holy grail at the moment)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Motivation for MSE (including formulae) ndash cont
prof James Davenport CEIC member MKM2011 PC chair on panel atEuDML workshop in Bertinoro as a reply to the question ldquowhat functionalityand incentives would made a working mathematician to login and use amodern DML as EuDMLrdquo
ldquoMath formulae searchrdquo
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why math search is more relevant now than ever
bull Allowing formulas in queries helps to disambiguate and narrow searchSometimes the only difference among set of notionskey words would bein a math formula
bull Example 1 knowing the solution of partial differential equation in L1(C3)is there one in L2(C5)
bull Example 2 historians may want to follow the history of a (class of) formula(s)across languages and vocabularies (eg same objects studiedused byphysicists and mathematicians under different names)
bull Imagine your favourite ebook math textbook being [TEX]-search awaremdashegyour search app supports math formulae search
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
We did not start from scratch
Compare googlecomsearchq=Einstein with math-awaresearch of Einstein+$E=mcˆ2$ over arXiv
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Existing systems ndash pros and cons
bull MathDex formerly MathFind seven digit figure NSF grant by Design Science(Robert Miner) Lucene based indexing n-grams of presentation MathML pioneering conversion effort
bull EgoMath and EgoMath2 based on full text web search system Egothor presentation MathML for indexing idea of formulae augmentation α-equivalencealgorithms and relevance calculation
bull LATEXSearch MSE offered by Springer closed source only for LATEX math stringapproximate match based on strings no formulae structure matching smalldatabase 3 million formulae from lsquorandomrsquo sources
bull LeActiveMath indexing string tokens from OMDoc with OpenMath semanticnotation only for documents authored for LeActiveMath learning environment
bull DLMF only for documents authored for DLMF in special markup equation search
bull MathWeb Search semantic approach ndash uses substitution trees ndash not based on full
text searching supports Content MathML and OpenMath problem with acquiring
semantic data
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MIaS mdash Math Indexer and Searcher
bull math-aware full-text based search engine
bull joins textual and mathematical querying
bull MathML or TEX input
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MSE overall design
input document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
math
searching
indexing
Lucene Core
canonicalization
mat
h
Preprocessing into
canonicalized presentation
MathML
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Math indexing design
input canonicalized
document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
mat
h
math
searching
indexing
Lucene
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization canonicalization
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Example
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization
searchingindexing
x y+y3
x y+y3 x y y3 x y 3+
x y+y3 x y y3 x y 3+ id1id 2+id 2
3 id 1id 2 id 1
3
x y+y3 x y y3 x y 3+ id1id 2+id 2
3
id1id 2 id1
3 x y+ yconst yconst id 1id 2+id 2
const id 1const
x y+y3
x y+y2
x y+y2
x y+y 2 id 1id 2+id 2
2
x y+y 2 id1id 2+id 2
2
x y+yconst id 1id 2+id 2
const
x y+yconst id 1id 2+id 2
const Match
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formula processing example ndash subformulae weighting
(a+b2+c 0125)
(a+bc+2 0125)
(a 00875) (+ 00875) (bc+2 00875)
(b 006125) (c+2 006125)
(c 0042875)(+ 0042875)
(2 0042875)
(id 1+2 00343)
(c+const 0030625)
(id 1+const 001715)
(id 1id 2+2 007)
(bc+const 004375)
(id 1id 2+const 0035)
(id 1+id 2id 3+2 01)
(a+bc+const 00625)
(id 1+id 2id 3+const 005)
input
ordering
tokenization
variablesunification
constantsunification
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Implementation
bull Java
bull Lucene 310 now switching to LuceneSolr 4
bull Mathematical part implements Lucenersquos interface Tokenizer ndash able tointegrate to any Lucene based system
bull MIaS4Solr plugin was created for the use in Solr
bull Textual content ndash processed by StandardAnalyzer
bull easily deployable in JavaLucene based system or as a web service
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Search demonstration
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formulae search demonstration comments
Demo web interface httpaurafimunicz8085webmias
bull MathMLTEX input (Tralics [2] for conversion to MathML [])
bull Canonicalization of the query ndash problems with UMCL library [1]
bull Matched document snippet generation
bull MathJax for nicer math rendering and better portability
bull Snuggle TeX for on-the-fly as-you-type rendering
All up and ready on the EuDML system
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Searching (semantically) similar papers
Exploration of a DML browsing (semantically) similar papers
Semantic search via topic modeling Latent Semantic Indexing LatentDirichlet Allocation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Leading Edge Example Automated Meaning Picking from Texts
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Probabilistic Topical Modeling Latent Dirichlet Allocation
bull topic weighted list of words
bull document weighted list of topics
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
lsquoBottom uprsquo deployment towards EU or worldwide scale
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
EuDML from local data collections to the virtual DL
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
From paper to digital workflow
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Retro-digitization accessible digital library development
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Experiences from project DML-CZ for EuDML (Brno CZ)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
EuDML new approaches to math document retrieval
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
New approaches to math-aware similarity clustering andaccessibility
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Tools for automated math extraction from PDF
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Yes you can lthttpeudmlorggt accessible math searchvisibility scalabilityhellip
TEX
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
End of talk overview
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
History of the dream vision of WDML as PubMed 4 Math
In the beginning was vision of all mathematical knowledge peer reviewedverified (100000000 pages) and engineered into one-stop e-shopDL
AMS supported NSF preparation grant (in 2003) for WDMLmdashWorldwidedigital mathematics library planned to be funded by de Moore foundation($100000000 requested) Application was not successful
Publishers started massive digitization themselves
Even other attempts on the European level (FP5 FP6) were not successful
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Vision of European Digital Mathematics Library
Finally three year project or European Digital Mathematics Library EuDML(programme EU CIP-ICT-PSP type Pilot B EU contribution (16 MEur 50of total budget only) February 2010ndashJanuary 2013 The strategy of
Final identity with strapline (stacked)
was
bull to master the technology develop tools and offer them
bull concept of moving wall to motivate and engage commercial publisherswithout Open Access bussiness model
bull to collect data (from existing local or publisherrsquos) digital libraries intolsquoone-stop shoprsquo and achieve critical mass in the domainrarr lsquoa mustmetoorsquo effect then as with PubMed Central
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
EuDML as a virtual library portal
EuDML provides a virtual library based on data from smaller data providersDLs and publishers
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
One portal European Digital Mathematics Library
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Aggregation of data from building bricks of regional repositories
14 data and technology providers plus associated partners as ZMathGoumlttingen libraryhellip
DML content providers serve mostly publisherrsquos or regional more or lessestablished DML repositories The Czech Digital Mathematics LibraryDML-CZ NUMDAM DML-PL DML-PT DML-GR DML-BG DML-EShellip
Aggregation via standard OAI-PMH protocol (OAI servers run by dataproviders)
EuDML metadata schema(s) was borrowed from NLM (heavily funded by USNiH) as it allows also math-awareness (eg math stored both in TEX andMathML) and fully fledged reference lists
Inovation rather than research Example of DML-CZ lthttpdmlczgt with30000+ papers (300000+ pages) For more see (who what browsebrowse similar how to search)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Challenges of Math handling OCR indexing searchhellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Take care ldquoGod is in the detailsrdquo (Mies van der Rohe)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Data heterogenity specificity no free lunch to unify
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Document accessibility 4 DML processing challenges
Conversions (inversion of authoring+typesetting) needed from
born-digital period typesetting by TEX with export of [meta]data into digitallibrary maxTract
retro-digital period scanning geometrical transformations (BookRestorer)OCR (FineReader InftyReader) two-layer PDF
retro-born-digital period not complete tex or dvi data bad formats bitmapfonts of low resolution finally Tesseract
andDocument
Metaminusdata
RETROminusBORN DIGITAL
Image
Extraction
OCR
DataPrinted Analysis
Conversion
RETROminusDIGITISED
BORN DIGITAL
Refinement
Printed
PDFPS
TeX
Image CaptureDocument
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
From PDF to MathML (via LATEX)
Most fulltext available as PDF only often as low quality scanned volumepages Aggregation via IP protected OAI-PMH including the PDFs behingmoving wall
Workflow based in the case of
born-digital PDFs on maxTract otherwise on PDFBox (plain text)
bitmap PDFs on Infty otherwise on Tesseract (no math)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Infty from Fukuoka
Run in parallel in Brno Grenoble and Lisbon to speed up Almost 200Kpapers (more than 1M pages and still running)
Working with prof Suzuki to improve further (automation support forRussian LATEX driverhellip)
Automated only no time (and money) to fix OCR errors
MathML output used for [internal] indexing and similarity computations onlynot for metadata or export
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham II adding accessibility
Adding accessibility to mathematical documents on multiple levels
bull access to content for print impaired users such as those with visualimpairments dyslexia or dyspraxia
bull output compatible with web browsers screen readers and tools such ascopy and paste which is achieved by enriching the regular text withmathematical markup The output can also be used directly within thelimits of the presentation MathML produced as machine readablemathematical input to software systems such as Mathematica or Maple
On EuDML 10k+ fulltexts are served mostly for reading in Chrome (HTML5output) andor Adobe Acrobat Reader (as multiple-layer PDFs [no taggedPDFs yet])
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Metadata and conversions MathML and LATEX
Data heterogenity plethora of formats validation and conversions
world of authors LATEX TEX notation of mathematics
world of applicationsdata exchange XML MathML
REPOX engine (by IST Lisbon) to remap different metadata formats to uniquerepresentation
Metadata on the webmdashW3C standards MathML WAI-ARIA (WebAccessibility InitiativemdashAcessible Rich Internet Applications) WCAG (WebContent Accessibility Guidelines) 20
Big volumes rarr high automation to save costs converting to MathML (viaTralics) to allow discoverability and indexing (formulae similarity search)130+K fulltexts with MathML Infty still runninghellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Search
Vast amounts of [moving] contents in digital libraries from browsing tosearch from static links to indirect search links or even semantic search
Searching is crucial part of accessibility and exploration of the great ideasaround carved into 0s and 1s
Pragmatic decisions on math indexing level presentation vs content vssemantic In EuDML first step scalable presentation (structural) withmethods (tree indexing and weighting) extendable for content or semantic
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Math Search (MIR)
A picture is worth thousands words
ldquoA math formulae is worth of hundreds of wordsldquo (Ross Moore)
There are papers with more formulae than plain text
Precision vs Recall optimisation optimizing recall is better for exploratorysearching (we have not opted for precision as holy grail at the moment)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Motivation for MSE (including formulae) ndash cont
prof James Davenport CEIC member MKM2011 PC chair on panel atEuDML workshop in Bertinoro as a reply to the question ldquowhat functionalityand incentives would made a working mathematician to login and use amodern DML as EuDMLrdquo
ldquoMath formulae searchrdquo
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why math search is more relevant now than ever
bull Allowing formulas in queries helps to disambiguate and narrow searchSometimes the only difference among set of notionskey words would bein a math formula
bull Example 1 knowing the solution of partial differential equation in L1(C3)is there one in L2(C5)
bull Example 2 historians may want to follow the history of a (class of) formula(s)across languages and vocabularies (eg same objects studiedused byphysicists and mathematicians under different names)
bull Imagine your favourite ebook math textbook being [TEX]-search awaremdashegyour search app supports math formulae search
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
We did not start from scratch
Compare googlecomsearchq=Einstein with math-awaresearch of Einstein+$E=mcˆ2$ over arXiv
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Existing systems ndash pros and cons
bull MathDex formerly MathFind seven digit figure NSF grant by Design Science(Robert Miner) Lucene based indexing n-grams of presentation MathML pioneering conversion effort
bull EgoMath and EgoMath2 based on full text web search system Egothor presentation MathML for indexing idea of formulae augmentation α-equivalencealgorithms and relevance calculation
bull LATEXSearch MSE offered by Springer closed source only for LATEX math stringapproximate match based on strings no formulae structure matching smalldatabase 3 million formulae from lsquorandomrsquo sources
bull LeActiveMath indexing string tokens from OMDoc with OpenMath semanticnotation only for documents authored for LeActiveMath learning environment
bull DLMF only for documents authored for DLMF in special markup equation search
bull MathWeb Search semantic approach ndash uses substitution trees ndash not based on full
text searching supports Content MathML and OpenMath problem with acquiring
semantic data
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MIaS mdash Math Indexer and Searcher
bull math-aware full-text based search engine
bull joins textual and mathematical querying
bull MathML or TEX input
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MSE overall design
input document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
math
searching
indexing
Lucene Core
canonicalization
mat
h
Preprocessing into
canonicalized presentation
MathML
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Math indexing design
input canonicalized
document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
mat
h
math
searching
indexing
Lucene
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization canonicalization
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Example
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization
searchingindexing
x y+y3
x y+y3 x y y3 x y 3+
x y+y3 x y y3 x y 3+ id1id 2+id 2
3 id 1id 2 id 1
3
x y+y3 x y y3 x y 3+ id1id 2+id 2
3
id1id 2 id1
3 x y+ yconst yconst id 1id 2+id 2
const id 1const
x y+y3
x y+y2
x y+y2
x y+y 2 id 1id 2+id 2
2
x y+y 2 id1id 2+id 2
2
x y+yconst id 1id 2+id 2
const
x y+yconst id 1id 2+id 2
const Match
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formula processing example ndash subformulae weighting
(a+b2+c 0125)
(a+bc+2 0125)
(a 00875) (+ 00875) (bc+2 00875)
(b 006125) (c+2 006125)
(c 0042875)(+ 0042875)
(2 0042875)
(id 1+2 00343)
(c+const 0030625)
(id 1+const 001715)
(id 1id 2+2 007)
(bc+const 004375)
(id 1id 2+const 0035)
(id 1+id 2id 3+2 01)
(a+bc+const 00625)
(id 1+id 2id 3+const 005)
input
ordering
tokenization
variablesunification
constantsunification
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Implementation
bull Java
bull Lucene 310 now switching to LuceneSolr 4
bull Mathematical part implements Lucenersquos interface Tokenizer ndash able tointegrate to any Lucene based system
bull MIaS4Solr plugin was created for the use in Solr
bull Textual content ndash processed by StandardAnalyzer
bull easily deployable in JavaLucene based system or as a web service
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Search demonstration
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formulae search demonstration comments
Demo web interface httpaurafimunicz8085webmias
bull MathMLTEX input (Tralics [2] for conversion to MathML [])
bull Canonicalization of the query ndash problems with UMCL library [1]
bull Matched document snippet generation
bull MathJax for nicer math rendering and better portability
bull Snuggle TeX for on-the-fly as-you-type rendering
All up and ready on the EuDML system
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Searching (semantically) similar papers
Exploration of a DML browsing (semantically) similar papers
Semantic search via topic modeling Latent Semantic Indexing LatentDirichlet Allocation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Leading Edge Example Automated Meaning Picking from Texts
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Probabilistic Topical Modeling Latent Dirichlet Allocation
bull topic weighted list of words
bull document weighted list of topics
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
EuDML from local data collections to the virtual DL
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
From paper to digital workflow
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Retro-digitization accessible digital library development
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Experiences from project DML-CZ for EuDML (Brno CZ)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
EuDML new approaches to math document retrieval
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
New approaches to math-aware similarity clustering andaccessibility
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Tools for automated math extraction from PDF
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Yes you can lthttpeudmlorggt accessible math searchvisibility scalabilityhellip
TEX
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
End of talk overview
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
History of the dream vision of WDML as PubMed 4 Math
In the beginning was vision of all mathematical knowledge peer reviewedverified (100000000 pages) and engineered into one-stop e-shopDL
AMS supported NSF preparation grant (in 2003) for WDMLmdashWorldwidedigital mathematics library planned to be funded by de Moore foundation($100000000 requested) Application was not successful
Publishers started massive digitization themselves
Even other attempts on the European level (FP5 FP6) were not successful
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Vision of European Digital Mathematics Library
Finally three year project or European Digital Mathematics Library EuDML(programme EU CIP-ICT-PSP type Pilot B EU contribution (16 MEur 50of total budget only) February 2010ndashJanuary 2013 The strategy of
Final identity with strapline (stacked)
was
bull to master the technology develop tools and offer them
bull concept of moving wall to motivate and engage commercial publisherswithout Open Access bussiness model
bull to collect data (from existing local or publisherrsquos) digital libraries intolsquoone-stop shoprsquo and achieve critical mass in the domainrarr lsquoa mustmetoorsquo effect then as with PubMed Central
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
EuDML as a virtual library portal
EuDML provides a virtual library based on data from smaller data providersDLs and publishers
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
One portal European Digital Mathematics Library
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Aggregation of data from building bricks of regional repositories
14 data and technology providers plus associated partners as ZMathGoumlttingen libraryhellip
DML content providers serve mostly publisherrsquos or regional more or lessestablished DML repositories The Czech Digital Mathematics LibraryDML-CZ NUMDAM DML-PL DML-PT DML-GR DML-BG DML-EShellip
Aggregation via standard OAI-PMH protocol (OAI servers run by dataproviders)
EuDML metadata schema(s) was borrowed from NLM (heavily funded by USNiH) as it allows also math-awareness (eg math stored both in TEX andMathML) and fully fledged reference lists
Inovation rather than research Example of DML-CZ lthttpdmlczgt with30000+ papers (300000+ pages) For more see (who what browsebrowse similar how to search)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Challenges of Math handling OCR indexing searchhellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Take care ldquoGod is in the detailsrdquo (Mies van der Rohe)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Data heterogenity specificity no free lunch to unify
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Document accessibility 4 DML processing challenges
Conversions (inversion of authoring+typesetting) needed from
born-digital period typesetting by TEX with export of [meta]data into digitallibrary maxTract
retro-digital period scanning geometrical transformations (BookRestorer)OCR (FineReader InftyReader) two-layer PDF
retro-born-digital period not complete tex or dvi data bad formats bitmapfonts of low resolution finally Tesseract
andDocument
Metaminusdata
RETROminusBORN DIGITAL
Image
Extraction
OCR
DataPrinted Analysis
Conversion
RETROminusDIGITISED
BORN DIGITAL
Refinement
Printed
PDFPS
TeX
Image CaptureDocument
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
From PDF to MathML (via LATEX)
Most fulltext available as PDF only often as low quality scanned volumepages Aggregation via IP protected OAI-PMH including the PDFs behingmoving wall
Workflow based in the case of
born-digital PDFs on maxTract otherwise on PDFBox (plain text)
bitmap PDFs on Infty otherwise on Tesseract (no math)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Infty from Fukuoka
Run in parallel in Brno Grenoble and Lisbon to speed up Almost 200Kpapers (more than 1M pages and still running)
Working with prof Suzuki to improve further (automation support forRussian LATEX driverhellip)
Automated only no time (and money) to fix OCR errors
MathML output used for [internal] indexing and similarity computations onlynot for metadata or export
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham II adding accessibility
Adding accessibility to mathematical documents on multiple levels
bull access to content for print impaired users such as those with visualimpairments dyslexia or dyspraxia
bull output compatible with web browsers screen readers and tools such ascopy and paste which is achieved by enriching the regular text withmathematical markup The output can also be used directly within thelimits of the presentation MathML produced as machine readablemathematical input to software systems such as Mathematica or Maple
On EuDML 10k+ fulltexts are served mostly for reading in Chrome (HTML5output) andor Adobe Acrobat Reader (as multiple-layer PDFs [no taggedPDFs yet])
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Metadata and conversions MathML and LATEX
Data heterogenity plethora of formats validation and conversions
world of authors LATEX TEX notation of mathematics
world of applicationsdata exchange XML MathML
REPOX engine (by IST Lisbon) to remap different metadata formats to uniquerepresentation
Metadata on the webmdashW3C standards MathML WAI-ARIA (WebAccessibility InitiativemdashAcessible Rich Internet Applications) WCAG (WebContent Accessibility Guidelines) 20
Big volumes rarr high automation to save costs converting to MathML (viaTralics) to allow discoverability and indexing (formulae similarity search)130+K fulltexts with MathML Infty still runninghellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Search
Vast amounts of [moving] contents in digital libraries from browsing tosearch from static links to indirect search links or even semantic search
Searching is crucial part of accessibility and exploration of the great ideasaround carved into 0s and 1s
Pragmatic decisions on math indexing level presentation vs content vssemantic In EuDML first step scalable presentation (structural) withmethods (tree indexing and weighting) extendable for content or semantic
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Math Search (MIR)
A picture is worth thousands words
ldquoA math formulae is worth of hundreds of wordsldquo (Ross Moore)
There are papers with more formulae than plain text
Precision vs Recall optimisation optimizing recall is better for exploratorysearching (we have not opted for precision as holy grail at the moment)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Motivation for MSE (including formulae) ndash cont
prof James Davenport CEIC member MKM2011 PC chair on panel atEuDML workshop in Bertinoro as a reply to the question ldquowhat functionalityand incentives would made a working mathematician to login and use amodern DML as EuDMLrdquo
ldquoMath formulae searchrdquo
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why math search is more relevant now than ever
bull Allowing formulas in queries helps to disambiguate and narrow searchSometimes the only difference among set of notionskey words would bein a math formula
bull Example 1 knowing the solution of partial differential equation in L1(C3)is there one in L2(C5)
bull Example 2 historians may want to follow the history of a (class of) formula(s)across languages and vocabularies (eg same objects studiedused byphysicists and mathematicians under different names)
bull Imagine your favourite ebook math textbook being [TEX]-search awaremdashegyour search app supports math formulae search
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
We did not start from scratch
Compare googlecomsearchq=Einstein with math-awaresearch of Einstein+$E=mcˆ2$ over arXiv
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Existing systems ndash pros and cons
bull MathDex formerly MathFind seven digit figure NSF grant by Design Science(Robert Miner) Lucene based indexing n-grams of presentation MathML pioneering conversion effort
bull EgoMath and EgoMath2 based on full text web search system Egothor presentation MathML for indexing idea of formulae augmentation α-equivalencealgorithms and relevance calculation
bull LATEXSearch MSE offered by Springer closed source only for LATEX math stringapproximate match based on strings no formulae structure matching smalldatabase 3 million formulae from lsquorandomrsquo sources
bull LeActiveMath indexing string tokens from OMDoc with OpenMath semanticnotation only for documents authored for LeActiveMath learning environment
bull DLMF only for documents authored for DLMF in special markup equation search
bull MathWeb Search semantic approach ndash uses substitution trees ndash not based on full
text searching supports Content MathML and OpenMath problem with acquiring
semantic data
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MIaS mdash Math Indexer and Searcher
bull math-aware full-text based search engine
bull joins textual and mathematical querying
bull MathML or TEX input
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MSE overall design
input document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
math
searching
indexing
Lucene Core
canonicalization
mat
h
Preprocessing into
canonicalized presentation
MathML
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Math indexing design
input canonicalized
document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
mat
h
math
searching
indexing
Lucene
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization canonicalization
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Example
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization
searchingindexing
x y+y3
x y+y3 x y y3 x y 3+
x y+y3 x y y3 x y 3+ id1id 2+id 2
3 id 1id 2 id 1
3
x y+y3 x y y3 x y 3+ id1id 2+id 2
3
id1id 2 id1
3 x y+ yconst yconst id 1id 2+id 2
const id 1const
x y+y3
x y+y2
x y+y2
x y+y 2 id 1id 2+id 2
2
x y+y 2 id1id 2+id 2
2
x y+yconst id 1id 2+id 2
const
x y+yconst id 1id 2+id 2
const Match
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formula processing example ndash subformulae weighting
(a+b2+c 0125)
(a+bc+2 0125)
(a 00875) (+ 00875) (bc+2 00875)
(b 006125) (c+2 006125)
(c 0042875)(+ 0042875)
(2 0042875)
(id 1+2 00343)
(c+const 0030625)
(id 1+const 001715)
(id 1id 2+2 007)
(bc+const 004375)
(id 1id 2+const 0035)
(id 1+id 2id 3+2 01)
(a+bc+const 00625)
(id 1+id 2id 3+const 005)
input
ordering
tokenization
variablesunification
constantsunification
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Implementation
bull Java
bull Lucene 310 now switching to LuceneSolr 4
bull Mathematical part implements Lucenersquos interface Tokenizer ndash able tointegrate to any Lucene based system
bull MIaS4Solr plugin was created for the use in Solr
bull Textual content ndash processed by StandardAnalyzer
bull easily deployable in JavaLucene based system or as a web service
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Search demonstration
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formulae search demonstration comments
Demo web interface httpaurafimunicz8085webmias
bull MathMLTEX input (Tralics [2] for conversion to MathML [])
bull Canonicalization of the query ndash problems with UMCL library [1]
bull Matched document snippet generation
bull MathJax for nicer math rendering and better portability
bull Snuggle TeX for on-the-fly as-you-type rendering
All up and ready on the EuDML system
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Searching (semantically) similar papers
Exploration of a DML browsing (semantically) similar papers
Semantic search via topic modeling Latent Semantic Indexing LatentDirichlet Allocation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Leading Edge Example Automated Meaning Picking from Texts
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Probabilistic Topical Modeling Latent Dirichlet Allocation
bull topic weighted list of words
bull document weighted list of topics
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
From paper to digital workflow
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Retro-digitization accessible digital library development
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Experiences from project DML-CZ for EuDML (Brno CZ)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
EuDML new approaches to math document retrieval
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
New approaches to math-aware similarity clustering andaccessibility
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Tools for automated math extraction from PDF
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Yes you can lthttpeudmlorggt accessible math searchvisibility scalabilityhellip
TEX
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
End of talk overview
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
History of the dream vision of WDML as PubMed 4 Math
In the beginning was vision of all mathematical knowledge peer reviewedverified (100000000 pages) and engineered into one-stop e-shopDL
AMS supported NSF preparation grant (in 2003) for WDMLmdashWorldwidedigital mathematics library planned to be funded by de Moore foundation($100000000 requested) Application was not successful
Publishers started massive digitization themselves
Even other attempts on the European level (FP5 FP6) were not successful
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Vision of European Digital Mathematics Library
Finally three year project or European Digital Mathematics Library EuDML(programme EU CIP-ICT-PSP type Pilot B EU contribution (16 MEur 50of total budget only) February 2010ndashJanuary 2013 The strategy of
Final identity with strapline (stacked)
was
bull to master the technology develop tools and offer them
bull concept of moving wall to motivate and engage commercial publisherswithout Open Access bussiness model
bull to collect data (from existing local or publisherrsquos) digital libraries intolsquoone-stop shoprsquo and achieve critical mass in the domainrarr lsquoa mustmetoorsquo effect then as with PubMed Central
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
EuDML as a virtual library portal
EuDML provides a virtual library based on data from smaller data providersDLs and publishers
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
One portal European Digital Mathematics Library
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Aggregation of data from building bricks of regional repositories
14 data and technology providers plus associated partners as ZMathGoumlttingen libraryhellip
DML content providers serve mostly publisherrsquos or regional more or lessestablished DML repositories The Czech Digital Mathematics LibraryDML-CZ NUMDAM DML-PL DML-PT DML-GR DML-BG DML-EShellip
Aggregation via standard OAI-PMH protocol (OAI servers run by dataproviders)
EuDML metadata schema(s) was borrowed from NLM (heavily funded by USNiH) as it allows also math-awareness (eg math stored both in TEX andMathML) and fully fledged reference lists
Inovation rather than research Example of DML-CZ lthttpdmlczgt with30000+ papers (300000+ pages) For more see (who what browsebrowse similar how to search)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Challenges of Math handling OCR indexing searchhellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Take care ldquoGod is in the detailsrdquo (Mies van der Rohe)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Data heterogenity specificity no free lunch to unify
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Document accessibility 4 DML processing challenges
Conversions (inversion of authoring+typesetting) needed from
born-digital period typesetting by TEX with export of [meta]data into digitallibrary maxTract
retro-digital period scanning geometrical transformations (BookRestorer)OCR (FineReader InftyReader) two-layer PDF
retro-born-digital period not complete tex or dvi data bad formats bitmapfonts of low resolution finally Tesseract
andDocument
Metaminusdata
RETROminusBORN DIGITAL
Image
Extraction
OCR
DataPrinted Analysis
Conversion
RETROminusDIGITISED
BORN DIGITAL
Refinement
Printed
PDFPS
TeX
Image CaptureDocument
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
From PDF to MathML (via LATEX)
Most fulltext available as PDF only often as low quality scanned volumepages Aggregation via IP protected OAI-PMH including the PDFs behingmoving wall
Workflow based in the case of
born-digital PDFs on maxTract otherwise on PDFBox (plain text)
bitmap PDFs on Infty otherwise on Tesseract (no math)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Infty from Fukuoka
Run in parallel in Brno Grenoble and Lisbon to speed up Almost 200Kpapers (more than 1M pages and still running)
Working with prof Suzuki to improve further (automation support forRussian LATEX driverhellip)
Automated only no time (and money) to fix OCR errors
MathML output used for [internal] indexing and similarity computations onlynot for metadata or export
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham II adding accessibility
Adding accessibility to mathematical documents on multiple levels
bull access to content for print impaired users such as those with visualimpairments dyslexia or dyspraxia
bull output compatible with web browsers screen readers and tools such ascopy and paste which is achieved by enriching the regular text withmathematical markup The output can also be used directly within thelimits of the presentation MathML produced as machine readablemathematical input to software systems such as Mathematica or Maple
On EuDML 10k+ fulltexts are served mostly for reading in Chrome (HTML5output) andor Adobe Acrobat Reader (as multiple-layer PDFs [no taggedPDFs yet])
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Metadata and conversions MathML and LATEX
Data heterogenity plethora of formats validation and conversions
world of authors LATEX TEX notation of mathematics
world of applicationsdata exchange XML MathML
REPOX engine (by IST Lisbon) to remap different metadata formats to uniquerepresentation
Metadata on the webmdashW3C standards MathML WAI-ARIA (WebAccessibility InitiativemdashAcessible Rich Internet Applications) WCAG (WebContent Accessibility Guidelines) 20
Big volumes rarr high automation to save costs converting to MathML (viaTralics) to allow discoverability and indexing (formulae similarity search)130+K fulltexts with MathML Infty still runninghellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Search
Vast amounts of [moving] contents in digital libraries from browsing tosearch from static links to indirect search links or even semantic search
Searching is crucial part of accessibility and exploration of the great ideasaround carved into 0s and 1s
Pragmatic decisions on math indexing level presentation vs content vssemantic In EuDML first step scalable presentation (structural) withmethods (tree indexing and weighting) extendable for content or semantic
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Math Search (MIR)
A picture is worth thousands words
ldquoA math formulae is worth of hundreds of wordsldquo (Ross Moore)
There are papers with more formulae than plain text
Precision vs Recall optimisation optimizing recall is better for exploratorysearching (we have not opted for precision as holy grail at the moment)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Motivation for MSE (including formulae) ndash cont
prof James Davenport CEIC member MKM2011 PC chair on panel atEuDML workshop in Bertinoro as a reply to the question ldquowhat functionalityand incentives would made a working mathematician to login and use amodern DML as EuDMLrdquo
ldquoMath formulae searchrdquo
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why math search is more relevant now than ever
bull Allowing formulas in queries helps to disambiguate and narrow searchSometimes the only difference among set of notionskey words would bein a math formula
bull Example 1 knowing the solution of partial differential equation in L1(C3)is there one in L2(C5)
bull Example 2 historians may want to follow the history of a (class of) formula(s)across languages and vocabularies (eg same objects studiedused byphysicists and mathematicians under different names)
bull Imagine your favourite ebook math textbook being [TEX]-search awaremdashegyour search app supports math formulae search
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
We did not start from scratch
Compare googlecomsearchq=Einstein with math-awaresearch of Einstein+$E=mcˆ2$ over arXiv
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Existing systems ndash pros and cons
bull MathDex formerly MathFind seven digit figure NSF grant by Design Science(Robert Miner) Lucene based indexing n-grams of presentation MathML pioneering conversion effort
bull EgoMath and EgoMath2 based on full text web search system Egothor presentation MathML for indexing idea of formulae augmentation α-equivalencealgorithms and relevance calculation
bull LATEXSearch MSE offered by Springer closed source only for LATEX math stringapproximate match based on strings no formulae structure matching smalldatabase 3 million formulae from lsquorandomrsquo sources
bull LeActiveMath indexing string tokens from OMDoc with OpenMath semanticnotation only for documents authored for LeActiveMath learning environment
bull DLMF only for documents authored for DLMF in special markup equation search
bull MathWeb Search semantic approach ndash uses substitution trees ndash not based on full
text searching supports Content MathML and OpenMath problem with acquiring
semantic data
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MIaS mdash Math Indexer and Searcher
bull math-aware full-text based search engine
bull joins textual and mathematical querying
bull MathML or TEX input
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MSE overall design
input document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
math
searching
indexing
Lucene Core
canonicalization
mat
h
Preprocessing into
canonicalized presentation
MathML
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Math indexing design
input canonicalized
document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
mat
h
math
searching
indexing
Lucene
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization canonicalization
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Example
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization
searchingindexing
x y+y3
x y+y3 x y y3 x y 3+
x y+y3 x y y3 x y 3+ id1id 2+id 2
3 id 1id 2 id 1
3
x y+y3 x y y3 x y 3+ id1id 2+id 2
3
id1id 2 id1
3 x y+ yconst yconst id 1id 2+id 2
const id 1const
x y+y3
x y+y2
x y+y2
x y+y 2 id 1id 2+id 2
2
x y+y 2 id1id 2+id 2
2
x y+yconst id 1id 2+id 2
const
x y+yconst id 1id 2+id 2
const Match
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formula processing example ndash subformulae weighting
(a+b2+c 0125)
(a+bc+2 0125)
(a 00875) (+ 00875) (bc+2 00875)
(b 006125) (c+2 006125)
(c 0042875)(+ 0042875)
(2 0042875)
(id 1+2 00343)
(c+const 0030625)
(id 1+const 001715)
(id 1id 2+2 007)
(bc+const 004375)
(id 1id 2+const 0035)
(id 1+id 2id 3+2 01)
(a+bc+const 00625)
(id 1+id 2id 3+const 005)
input
ordering
tokenization
variablesunification
constantsunification
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Implementation
bull Java
bull Lucene 310 now switching to LuceneSolr 4
bull Mathematical part implements Lucenersquos interface Tokenizer ndash able tointegrate to any Lucene based system
bull MIaS4Solr plugin was created for the use in Solr
bull Textual content ndash processed by StandardAnalyzer
bull easily deployable in JavaLucene based system or as a web service
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Search demonstration
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formulae search demonstration comments
Demo web interface httpaurafimunicz8085webmias
bull MathMLTEX input (Tralics [2] for conversion to MathML [])
bull Canonicalization of the query ndash problems with UMCL library [1]
bull Matched document snippet generation
bull MathJax for nicer math rendering and better portability
bull Snuggle TeX for on-the-fly as-you-type rendering
All up and ready on the EuDML system
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Searching (semantically) similar papers
Exploration of a DML browsing (semantically) similar papers
Semantic search via topic modeling Latent Semantic Indexing LatentDirichlet Allocation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Leading Edge Example Automated Meaning Picking from Texts
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Probabilistic Topical Modeling Latent Dirichlet Allocation
bull topic weighted list of words
bull document weighted list of topics
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Retro-digitization accessible digital library development
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Experiences from project DML-CZ for EuDML (Brno CZ)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
EuDML new approaches to math document retrieval
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
New approaches to math-aware similarity clustering andaccessibility
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Tools for automated math extraction from PDF
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Yes you can lthttpeudmlorggt accessible math searchvisibility scalabilityhellip
TEX
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
End of talk overview
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
History of the dream vision of WDML as PubMed 4 Math
In the beginning was vision of all mathematical knowledge peer reviewedverified (100000000 pages) and engineered into one-stop e-shopDL
AMS supported NSF preparation grant (in 2003) for WDMLmdashWorldwidedigital mathematics library planned to be funded by de Moore foundation($100000000 requested) Application was not successful
Publishers started massive digitization themselves
Even other attempts on the European level (FP5 FP6) were not successful
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Vision of European Digital Mathematics Library
Finally three year project or European Digital Mathematics Library EuDML(programme EU CIP-ICT-PSP type Pilot B EU contribution (16 MEur 50of total budget only) February 2010ndashJanuary 2013 The strategy of
Final identity with strapline (stacked)
was
bull to master the technology develop tools and offer them
bull concept of moving wall to motivate and engage commercial publisherswithout Open Access bussiness model
bull to collect data (from existing local or publisherrsquos) digital libraries intolsquoone-stop shoprsquo and achieve critical mass in the domainrarr lsquoa mustmetoorsquo effect then as with PubMed Central
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
EuDML as a virtual library portal
EuDML provides a virtual library based on data from smaller data providersDLs and publishers
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
One portal European Digital Mathematics Library
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Aggregation of data from building bricks of regional repositories
14 data and technology providers plus associated partners as ZMathGoumlttingen libraryhellip
DML content providers serve mostly publisherrsquos or regional more or lessestablished DML repositories The Czech Digital Mathematics LibraryDML-CZ NUMDAM DML-PL DML-PT DML-GR DML-BG DML-EShellip
Aggregation via standard OAI-PMH protocol (OAI servers run by dataproviders)
EuDML metadata schema(s) was borrowed from NLM (heavily funded by USNiH) as it allows also math-awareness (eg math stored both in TEX andMathML) and fully fledged reference lists
Inovation rather than research Example of DML-CZ lthttpdmlczgt with30000+ papers (300000+ pages) For more see (who what browsebrowse similar how to search)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Challenges of Math handling OCR indexing searchhellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Take care ldquoGod is in the detailsrdquo (Mies van der Rohe)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Data heterogenity specificity no free lunch to unify
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Document accessibility 4 DML processing challenges
Conversions (inversion of authoring+typesetting) needed from
born-digital period typesetting by TEX with export of [meta]data into digitallibrary maxTract
retro-digital period scanning geometrical transformations (BookRestorer)OCR (FineReader InftyReader) two-layer PDF
retro-born-digital period not complete tex or dvi data bad formats bitmapfonts of low resolution finally Tesseract
andDocument
Metaminusdata
RETROminusBORN DIGITAL
Image
Extraction
OCR
DataPrinted Analysis
Conversion
RETROminusDIGITISED
BORN DIGITAL
Refinement
Printed
PDFPS
TeX
Image CaptureDocument
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
From PDF to MathML (via LATEX)
Most fulltext available as PDF only often as low quality scanned volumepages Aggregation via IP protected OAI-PMH including the PDFs behingmoving wall
Workflow based in the case of
born-digital PDFs on maxTract otherwise on PDFBox (plain text)
bitmap PDFs on Infty otherwise on Tesseract (no math)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Infty from Fukuoka
Run in parallel in Brno Grenoble and Lisbon to speed up Almost 200Kpapers (more than 1M pages and still running)
Working with prof Suzuki to improve further (automation support forRussian LATEX driverhellip)
Automated only no time (and money) to fix OCR errors
MathML output used for [internal] indexing and similarity computations onlynot for metadata or export
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham II adding accessibility
Adding accessibility to mathematical documents on multiple levels
bull access to content for print impaired users such as those with visualimpairments dyslexia or dyspraxia
bull output compatible with web browsers screen readers and tools such ascopy and paste which is achieved by enriching the regular text withmathematical markup The output can also be used directly within thelimits of the presentation MathML produced as machine readablemathematical input to software systems such as Mathematica or Maple
On EuDML 10k+ fulltexts are served mostly for reading in Chrome (HTML5output) andor Adobe Acrobat Reader (as multiple-layer PDFs [no taggedPDFs yet])
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Metadata and conversions MathML and LATEX
Data heterogenity plethora of formats validation and conversions
world of authors LATEX TEX notation of mathematics
world of applicationsdata exchange XML MathML
REPOX engine (by IST Lisbon) to remap different metadata formats to uniquerepresentation
Metadata on the webmdashW3C standards MathML WAI-ARIA (WebAccessibility InitiativemdashAcessible Rich Internet Applications) WCAG (WebContent Accessibility Guidelines) 20
Big volumes rarr high automation to save costs converting to MathML (viaTralics) to allow discoverability and indexing (formulae similarity search)130+K fulltexts with MathML Infty still runninghellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Search
Vast amounts of [moving] contents in digital libraries from browsing tosearch from static links to indirect search links or even semantic search
Searching is crucial part of accessibility and exploration of the great ideasaround carved into 0s and 1s
Pragmatic decisions on math indexing level presentation vs content vssemantic In EuDML first step scalable presentation (structural) withmethods (tree indexing and weighting) extendable for content or semantic
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Math Search (MIR)
A picture is worth thousands words
ldquoA math formulae is worth of hundreds of wordsldquo (Ross Moore)
There are papers with more formulae than plain text
Precision vs Recall optimisation optimizing recall is better for exploratorysearching (we have not opted for precision as holy grail at the moment)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Motivation for MSE (including formulae) ndash cont
prof James Davenport CEIC member MKM2011 PC chair on panel atEuDML workshop in Bertinoro as a reply to the question ldquowhat functionalityand incentives would made a working mathematician to login and use amodern DML as EuDMLrdquo
ldquoMath formulae searchrdquo
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why math search is more relevant now than ever
bull Allowing formulas in queries helps to disambiguate and narrow searchSometimes the only difference among set of notionskey words would bein a math formula
bull Example 1 knowing the solution of partial differential equation in L1(C3)is there one in L2(C5)
bull Example 2 historians may want to follow the history of a (class of) formula(s)across languages and vocabularies (eg same objects studiedused byphysicists and mathematicians under different names)
bull Imagine your favourite ebook math textbook being [TEX]-search awaremdashegyour search app supports math formulae search
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
We did not start from scratch
Compare googlecomsearchq=Einstein with math-awaresearch of Einstein+$E=mcˆ2$ over arXiv
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Existing systems ndash pros and cons
bull MathDex formerly MathFind seven digit figure NSF grant by Design Science(Robert Miner) Lucene based indexing n-grams of presentation MathML pioneering conversion effort
bull EgoMath and EgoMath2 based on full text web search system Egothor presentation MathML for indexing idea of formulae augmentation α-equivalencealgorithms and relevance calculation
bull LATEXSearch MSE offered by Springer closed source only for LATEX math stringapproximate match based on strings no formulae structure matching smalldatabase 3 million formulae from lsquorandomrsquo sources
bull LeActiveMath indexing string tokens from OMDoc with OpenMath semanticnotation only for documents authored for LeActiveMath learning environment
bull DLMF only for documents authored for DLMF in special markup equation search
bull MathWeb Search semantic approach ndash uses substitution trees ndash not based on full
text searching supports Content MathML and OpenMath problem with acquiring
semantic data
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MIaS mdash Math Indexer and Searcher
bull math-aware full-text based search engine
bull joins textual and mathematical querying
bull MathML or TEX input
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MSE overall design
input document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
math
searching
indexing
Lucene Core
canonicalization
mat
h
Preprocessing into
canonicalized presentation
MathML
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Math indexing design
input canonicalized
document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
mat
h
math
searching
indexing
Lucene
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization canonicalization
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Example
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization
searchingindexing
x y+y3
x y+y3 x y y3 x y 3+
x y+y3 x y y3 x y 3+ id1id 2+id 2
3 id 1id 2 id 1
3
x y+y3 x y y3 x y 3+ id1id 2+id 2
3
id1id 2 id1
3 x y+ yconst yconst id 1id 2+id 2
const id 1const
x y+y3
x y+y2
x y+y2
x y+y 2 id 1id 2+id 2
2
x y+y 2 id1id 2+id 2
2
x y+yconst id 1id 2+id 2
const
x y+yconst id 1id 2+id 2
const Match
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formula processing example ndash subformulae weighting
(a+b2+c 0125)
(a+bc+2 0125)
(a 00875) (+ 00875) (bc+2 00875)
(b 006125) (c+2 006125)
(c 0042875)(+ 0042875)
(2 0042875)
(id 1+2 00343)
(c+const 0030625)
(id 1+const 001715)
(id 1id 2+2 007)
(bc+const 004375)
(id 1id 2+const 0035)
(id 1+id 2id 3+2 01)
(a+bc+const 00625)
(id 1+id 2id 3+const 005)
input
ordering
tokenization
variablesunification
constantsunification
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Implementation
bull Java
bull Lucene 310 now switching to LuceneSolr 4
bull Mathematical part implements Lucenersquos interface Tokenizer ndash able tointegrate to any Lucene based system
bull MIaS4Solr plugin was created for the use in Solr
bull Textual content ndash processed by StandardAnalyzer
bull easily deployable in JavaLucene based system or as a web service
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Search demonstration
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formulae search demonstration comments
Demo web interface httpaurafimunicz8085webmias
bull MathMLTEX input (Tralics [2] for conversion to MathML [])
bull Canonicalization of the query ndash problems with UMCL library [1]
bull Matched document snippet generation
bull MathJax for nicer math rendering and better portability
bull Snuggle TeX for on-the-fly as-you-type rendering
All up and ready on the EuDML system
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Searching (semantically) similar papers
Exploration of a DML browsing (semantically) similar papers
Semantic search via topic modeling Latent Semantic Indexing LatentDirichlet Allocation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Leading Edge Example Automated Meaning Picking from Texts
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Probabilistic Topical Modeling Latent Dirichlet Allocation
bull topic weighted list of words
bull document weighted list of topics
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Experiences from project DML-CZ for EuDML (Brno CZ)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
EuDML new approaches to math document retrieval
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
New approaches to math-aware similarity clustering andaccessibility
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Tools for automated math extraction from PDF
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Yes you can lthttpeudmlorggt accessible math searchvisibility scalabilityhellip
TEX
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
End of talk overview
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
History of the dream vision of WDML as PubMed 4 Math
In the beginning was vision of all mathematical knowledge peer reviewedverified (100000000 pages) and engineered into one-stop e-shopDL
AMS supported NSF preparation grant (in 2003) for WDMLmdashWorldwidedigital mathematics library planned to be funded by de Moore foundation($100000000 requested) Application was not successful
Publishers started massive digitization themselves
Even other attempts on the European level (FP5 FP6) were not successful
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Vision of European Digital Mathematics Library
Finally three year project or European Digital Mathematics Library EuDML(programme EU CIP-ICT-PSP type Pilot B EU contribution (16 MEur 50of total budget only) February 2010ndashJanuary 2013 The strategy of
Final identity with strapline (stacked)
was
bull to master the technology develop tools and offer them
bull concept of moving wall to motivate and engage commercial publisherswithout Open Access bussiness model
bull to collect data (from existing local or publisherrsquos) digital libraries intolsquoone-stop shoprsquo and achieve critical mass in the domainrarr lsquoa mustmetoorsquo effect then as with PubMed Central
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
EuDML as a virtual library portal
EuDML provides a virtual library based on data from smaller data providersDLs and publishers
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
One portal European Digital Mathematics Library
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Aggregation of data from building bricks of regional repositories
14 data and technology providers plus associated partners as ZMathGoumlttingen libraryhellip
DML content providers serve mostly publisherrsquos or regional more or lessestablished DML repositories The Czech Digital Mathematics LibraryDML-CZ NUMDAM DML-PL DML-PT DML-GR DML-BG DML-EShellip
Aggregation via standard OAI-PMH protocol (OAI servers run by dataproviders)
EuDML metadata schema(s) was borrowed from NLM (heavily funded by USNiH) as it allows also math-awareness (eg math stored both in TEX andMathML) and fully fledged reference lists
Inovation rather than research Example of DML-CZ lthttpdmlczgt with30000+ papers (300000+ pages) For more see (who what browsebrowse similar how to search)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Challenges of Math handling OCR indexing searchhellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Take care ldquoGod is in the detailsrdquo (Mies van der Rohe)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Data heterogenity specificity no free lunch to unify
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Document accessibility 4 DML processing challenges
Conversions (inversion of authoring+typesetting) needed from
born-digital period typesetting by TEX with export of [meta]data into digitallibrary maxTract
retro-digital period scanning geometrical transformations (BookRestorer)OCR (FineReader InftyReader) two-layer PDF
retro-born-digital period not complete tex or dvi data bad formats bitmapfonts of low resolution finally Tesseract
andDocument
Metaminusdata
RETROminusBORN DIGITAL
Image
Extraction
OCR
DataPrinted Analysis
Conversion
RETROminusDIGITISED
BORN DIGITAL
Refinement
Printed
PDFPS
TeX
Image CaptureDocument
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
From PDF to MathML (via LATEX)
Most fulltext available as PDF only often as low quality scanned volumepages Aggregation via IP protected OAI-PMH including the PDFs behingmoving wall
Workflow based in the case of
born-digital PDFs on maxTract otherwise on PDFBox (plain text)
bitmap PDFs on Infty otherwise on Tesseract (no math)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Infty from Fukuoka
Run in parallel in Brno Grenoble and Lisbon to speed up Almost 200Kpapers (more than 1M pages and still running)
Working with prof Suzuki to improve further (automation support forRussian LATEX driverhellip)
Automated only no time (and money) to fix OCR errors
MathML output used for [internal] indexing and similarity computations onlynot for metadata or export
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham II adding accessibility
Adding accessibility to mathematical documents on multiple levels
bull access to content for print impaired users such as those with visualimpairments dyslexia or dyspraxia
bull output compatible with web browsers screen readers and tools such ascopy and paste which is achieved by enriching the regular text withmathematical markup The output can also be used directly within thelimits of the presentation MathML produced as machine readablemathematical input to software systems such as Mathematica or Maple
On EuDML 10k+ fulltexts are served mostly for reading in Chrome (HTML5output) andor Adobe Acrobat Reader (as multiple-layer PDFs [no taggedPDFs yet])
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Metadata and conversions MathML and LATEX
Data heterogenity plethora of formats validation and conversions
world of authors LATEX TEX notation of mathematics
world of applicationsdata exchange XML MathML
REPOX engine (by IST Lisbon) to remap different metadata formats to uniquerepresentation
Metadata on the webmdashW3C standards MathML WAI-ARIA (WebAccessibility InitiativemdashAcessible Rich Internet Applications) WCAG (WebContent Accessibility Guidelines) 20
Big volumes rarr high automation to save costs converting to MathML (viaTralics) to allow discoverability and indexing (formulae similarity search)130+K fulltexts with MathML Infty still runninghellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Search
Vast amounts of [moving] contents in digital libraries from browsing tosearch from static links to indirect search links or even semantic search
Searching is crucial part of accessibility and exploration of the great ideasaround carved into 0s and 1s
Pragmatic decisions on math indexing level presentation vs content vssemantic In EuDML first step scalable presentation (structural) withmethods (tree indexing and weighting) extendable for content or semantic
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Math Search (MIR)
A picture is worth thousands words
ldquoA math formulae is worth of hundreds of wordsldquo (Ross Moore)
There are papers with more formulae than plain text
Precision vs Recall optimisation optimizing recall is better for exploratorysearching (we have not opted for precision as holy grail at the moment)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Motivation for MSE (including formulae) ndash cont
prof James Davenport CEIC member MKM2011 PC chair on panel atEuDML workshop in Bertinoro as a reply to the question ldquowhat functionalityand incentives would made a working mathematician to login and use amodern DML as EuDMLrdquo
ldquoMath formulae searchrdquo
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why math search is more relevant now than ever
bull Allowing formulas in queries helps to disambiguate and narrow searchSometimes the only difference among set of notionskey words would bein a math formula
bull Example 1 knowing the solution of partial differential equation in L1(C3)is there one in L2(C5)
bull Example 2 historians may want to follow the history of a (class of) formula(s)across languages and vocabularies (eg same objects studiedused byphysicists and mathematicians under different names)
bull Imagine your favourite ebook math textbook being [TEX]-search awaremdashegyour search app supports math formulae search
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
We did not start from scratch
Compare googlecomsearchq=Einstein with math-awaresearch of Einstein+$E=mcˆ2$ over arXiv
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Existing systems ndash pros and cons
bull MathDex formerly MathFind seven digit figure NSF grant by Design Science(Robert Miner) Lucene based indexing n-grams of presentation MathML pioneering conversion effort
bull EgoMath and EgoMath2 based on full text web search system Egothor presentation MathML for indexing idea of formulae augmentation α-equivalencealgorithms and relevance calculation
bull LATEXSearch MSE offered by Springer closed source only for LATEX math stringapproximate match based on strings no formulae structure matching smalldatabase 3 million formulae from lsquorandomrsquo sources
bull LeActiveMath indexing string tokens from OMDoc with OpenMath semanticnotation only for documents authored for LeActiveMath learning environment
bull DLMF only for documents authored for DLMF in special markup equation search
bull MathWeb Search semantic approach ndash uses substitution trees ndash not based on full
text searching supports Content MathML and OpenMath problem with acquiring
semantic data
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MIaS mdash Math Indexer and Searcher
bull math-aware full-text based search engine
bull joins textual and mathematical querying
bull MathML or TEX input
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MSE overall design
input document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
math
searching
indexing
Lucene Core
canonicalization
mat
h
Preprocessing into
canonicalized presentation
MathML
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Math indexing design
input canonicalized
document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
mat
h
math
searching
indexing
Lucene
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization canonicalization
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Example
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization
searchingindexing
x y+y3
x y+y3 x y y3 x y 3+
x y+y3 x y y3 x y 3+ id1id 2+id 2
3 id 1id 2 id 1
3
x y+y3 x y y3 x y 3+ id1id 2+id 2
3
id1id 2 id1
3 x y+ yconst yconst id 1id 2+id 2
const id 1const
x y+y3
x y+y2
x y+y2
x y+y 2 id 1id 2+id 2
2
x y+y 2 id1id 2+id 2
2
x y+yconst id 1id 2+id 2
const
x y+yconst id 1id 2+id 2
const Match
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formula processing example ndash subformulae weighting
(a+b2+c 0125)
(a+bc+2 0125)
(a 00875) (+ 00875) (bc+2 00875)
(b 006125) (c+2 006125)
(c 0042875)(+ 0042875)
(2 0042875)
(id 1+2 00343)
(c+const 0030625)
(id 1+const 001715)
(id 1id 2+2 007)
(bc+const 004375)
(id 1id 2+const 0035)
(id 1+id 2id 3+2 01)
(a+bc+const 00625)
(id 1+id 2id 3+const 005)
input
ordering
tokenization
variablesunification
constantsunification
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Implementation
bull Java
bull Lucene 310 now switching to LuceneSolr 4
bull Mathematical part implements Lucenersquos interface Tokenizer ndash able tointegrate to any Lucene based system
bull MIaS4Solr plugin was created for the use in Solr
bull Textual content ndash processed by StandardAnalyzer
bull easily deployable in JavaLucene based system or as a web service
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Search demonstration
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formulae search demonstration comments
Demo web interface httpaurafimunicz8085webmias
bull MathMLTEX input (Tralics [2] for conversion to MathML [])
bull Canonicalization of the query ndash problems with UMCL library [1]
bull Matched document snippet generation
bull MathJax for nicer math rendering and better portability
bull Snuggle TeX for on-the-fly as-you-type rendering
All up and ready on the EuDML system
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Searching (semantically) similar papers
Exploration of a DML browsing (semantically) similar papers
Semantic search via topic modeling Latent Semantic Indexing LatentDirichlet Allocation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Leading Edge Example Automated Meaning Picking from Texts
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Probabilistic Topical Modeling Latent Dirichlet Allocation
bull topic weighted list of words
bull document weighted list of topics
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
EuDML new approaches to math document retrieval
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
New approaches to math-aware similarity clustering andaccessibility
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Tools for automated math extraction from PDF
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Yes you can lthttpeudmlorggt accessible math searchvisibility scalabilityhellip
TEX
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
End of talk overview
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
History of the dream vision of WDML as PubMed 4 Math
In the beginning was vision of all mathematical knowledge peer reviewedverified (100000000 pages) and engineered into one-stop e-shopDL
AMS supported NSF preparation grant (in 2003) for WDMLmdashWorldwidedigital mathematics library planned to be funded by de Moore foundation($100000000 requested) Application was not successful
Publishers started massive digitization themselves
Even other attempts on the European level (FP5 FP6) were not successful
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Vision of European Digital Mathematics Library
Finally three year project or European Digital Mathematics Library EuDML(programme EU CIP-ICT-PSP type Pilot B EU contribution (16 MEur 50of total budget only) February 2010ndashJanuary 2013 The strategy of
Final identity with strapline (stacked)
was
bull to master the technology develop tools and offer them
bull concept of moving wall to motivate and engage commercial publisherswithout Open Access bussiness model
bull to collect data (from existing local or publisherrsquos) digital libraries intolsquoone-stop shoprsquo and achieve critical mass in the domainrarr lsquoa mustmetoorsquo effect then as with PubMed Central
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
EuDML as a virtual library portal
EuDML provides a virtual library based on data from smaller data providersDLs and publishers
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
One portal European Digital Mathematics Library
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Aggregation of data from building bricks of regional repositories
14 data and technology providers plus associated partners as ZMathGoumlttingen libraryhellip
DML content providers serve mostly publisherrsquos or regional more or lessestablished DML repositories The Czech Digital Mathematics LibraryDML-CZ NUMDAM DML-PL DML-PT DML-GR DML-BG DML-EShellip
Aggregation via standard OAI-PMH protocol (OAI servers run by dataproviders)
EuDML metadata schema(s) was borrowed from NLM (heavily funded by USNiH) as it allows also math-awareness (eg math stored both in TEX andMathML) and fully fledged reference lists
Inovation rather than research Example of DML-CZ lthttpdmlczgt with30000+ papers (300000+ pages) For more see (who what browsebrowse similar how to search)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Challenges of Math handling OCR indexing searchhellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Take care ldquoGod is in the detailsrdquo (Mies van der Rohe)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Data heterogenity specificity no free lunch to unify
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Document accessibility 4 DML processing challenges
Conversions (inversion of authoring+typesetting) needed from
born-digital period typesetting by TEX with export of [meta]data into digitallibrary maxTract
retro-digital period scanning geometrical transformations (BookRestorer)OCR (FineReader InftyReader) two-layer PDF
retro-born-digital period not complete tex or dvi data bad formats bitmapfonts of low resolution finally Tesseract
andDocument
Metaminusdata
RETROminusBORN DIGITAL
Image
Extraction
OCR
DataPrinted Analysis
Conversion
RETROminusDIGITISED
BORN DIGITAL
Refinement
Printed
PDFPS
TeX
Image CaptureDocument
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
From PDF to MathML (via LATEX)
Most fulltext available as PDF only often as low quality scanned volumepages Aggregation via IP protected OAI-PMH including the PDFs behingmoving wall
Workflow based in the case of
born-digital PDFs on maxTract otherwise on PDFBox (plain text)
bitmap PDFs on Infty otherwise on Tesseract (no math)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Infty from Fukuoka
Run in parallel in Brno Grenoble and Lisbon to speed up Almost 200Kpapers (more than 1M pages and still running)
Working with prof Suzuki to improve further (automation support forRussian LATEX driverhellip)
Automated only no time (and money) to fix OCR errors
MathML output used for [internal] indexing and similarity computations onlynot for metadata or export
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham II adding accessibility
Adding accessibility to mathematical documents on multiple levels
bull access to content for print impaired users such as those with visualimpairments dyslexia or dyspraxia
bull output compatible with web browsers screen readers and tools such ascopy and paste which is achieved by enriching the regular text withmathematical markup The output can also be used directly within thelimits of the presentation MathML produced as machine readablemathematical input to software systems such as Mathematica or Maple
On EuDML 10k+ fulltexts are served mostly for reading in Chrome (HTML5output) andor Adobe Acrobat Reader (as multiple-layer PDFs [no taggedPDFs yet])
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Metadata and conversions MathML and LATEX
Data heterogenity plethora of formats validation and conversions
world of authors LATEX TEX notation of mathematics
world of applicationsdata exchange XML MathML
REPOX engine (by IST Lisbon) to remap different metadata formats to uniquerepresentation
Metadata on the webmdashW3C standards MathML WAI-ARIA (WebAccessibility InitiativemdashAcessible Rich Internet Applications) WCAG (WebContent Accessibility Guidelines) 20
Big volumes rarr high automation to save costs converting to MathML (viaTralics) to allow discoverability and indexing (formulae similarity search)130+K fulltexts with MathML Infty still runninghellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Search
Vast amounts of [moving] contents in digital libraries from browsing tosearch from static links to indirect search links or even semantic search
Searching is crucial part of accessibility and exploration of the great ideasaround carved into 0s and 1s
Pragmatic decisions on math indexing level presentation vs content vssemantic In EuDML first step scalable presentation (structural) withmethods (tree indexing and weighting) extendable for content or semantic
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Math Search (MIR)
A picture is worth thousands words
ldquoA math formulae is worth of hundreds of wordsldquo (Ross Moore)
There are papers with more formulae than plain text
Precision vs Recall optimisation optimizing recall is better for exploratorysearching (we have not opted for precision as holy grail at the moment)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Motivation for MSE (including formulae) ndash cont
prof James Davenport CEIC member MKM2011 PC chair on panel atEuDML workshop in Bertinoro as a reply to the question ldquowhat functionalityand incentives would made a working mathematician to login and use amodern DML as EuDMLrdquo
ldquoMath formulae searchrdquo
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why math search is more relevant now than ever
bull Allowing formulas in queries helps to disambiguate and narrow searchSometimes the only difference among set of notionskey words would bein a math formula
bull Example 1 knowing the solution of partial differential equation in L1(C3)is there one in L2(C5)
bull Example 2 historians may want to follow the history of a (class of) formula(s)across languages and vocabularies (eg same objects studiedused byphysicists and mathematicians under different names)
bull Imagine your favourite ebook math textbook being [TEX]-search awaremdashegyour search app supports math formulae search
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
We did not start from scratch
Compare googlecomsearchq=Einstein with math-awaresearch of Einstein+$E=mcˆ2$ over arXiv
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Existing systems ndash pros and cons
bull MathDex formerly MathFind seven digit figure NSF grant by Design Science(Robert Miner) Lucene based indexing n-grams of presentation MathML pioneering conversion effort
bull EgoMath and EgoMath2 based on full text web search system Egothor presentation MathML for indexing idea of formulae augmentation α-equivalencealgorithms and relevance calculation
bull LATEXSearch MSE offered by Springer closed source only for LATEX math stringapproximate match based on strings no formulae structure matching smalldatabase 3 million formulae from lsquorandomrsquo sources
bull LeActiveMath indexing string tokens from OMDoc with OpenMath semanticnotation only for documents authored for LeActiveMath learning environment
bull DLMF only for documents authored for DLMF in special markup equation search
bull MathWeb Search semantic approach ndash uses substitution trees ndash not based on full
text searching supports Content MathML and OpenMath problem with acquiring
semantic data
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MIaS mdash Math Indexer and Searcher
bull math-aware full-text based search engine
bull joins textual and mathematical querying
bull MathML or TEX input
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MSE overall design
input document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
math
searching
indexing
Lucene Core
canonicalization
mat
h
Preprocessing into
canonicalized presentation
MathML
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Math indexing design
input canonicalized
document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
mat
h
math
searching
indexing
Lucene
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization canonicalization
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Example
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization
searchingindexing
x y+y3
x y+y3 x y y3 x y 3+
x y+y3 x y y3 x y 3+ id1id 2+id 2
3 id 1id 2 id 1
3
x y+y3 x y y3 x y 3+ id1id 2+id 2
3
id1id 2 id1
3 x y+ yconst yconst id 1id 2+id 2
const id 1const
x y+y3
x y+y2
x y+y2
x y+y 2 id 1id 2+id 2
2
x y+y 2 id1id 2+id 2
2
x y+yconst id 1id 2+id 2
const
x y+yconst id 1id 2+id 2
const Match
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formula processing example ndash subformulae weighting
(a+b2+c 0125)
(a+bc+2 0125)
(a 00875) (+ 00875) (bc+2 00875)
(b 006125) (c+2 006125)
(c 0042875)(+ 0042875)
(2 0042875)
(id 1+2 00343)
(c+const 0030625)
(id 1+const 001715)
(id 1id 2+2 007)
(bc+const 004375)
(id 1id 2+const 0035)
(id 1+id 2id 3+2 01)
(a+bc+const 00625)
(id 1+id 2id 3+const 005)
input
ordering
tokenization
variablesunification
constantsunification
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Implementation
bull Java
bull Lucene 310 now switching to LuceneSolr 4
bull Mathematical part implements Lucenersquos interface Tokenizer ndash able tointegrate to any Lucene based system
bull MIaS4Solr plugin was created for the use in Solr
bull Textual content ndash processed by StandardAnalyzer
bull easily deployable in JavaLucene based system or as a web service
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Search demonstration
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formulae search demonstration comments
Demo web interface httpaurafimunicz8085webmias
bull MathMLTEX input (Tralics [2] for conversion to MathML [])
bull Canonicalization of the query ndash problems with UMCL library [1]
bull Matched document snippet generation
bull MathJax for nicer math rendering and better portability
bull Snuggle TeX for on-the-fly as-you-type rendering
All up and ready on the EuDML system
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Searching (semantically) similar papers
Exploration of a DML browsing (semantically) similar papers
Semantic search via topic modeling Latent Semantic Indexing LatentDirichlet Allocation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Leading Edge Example Automated Meaning Picking from Texts
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Probabilistic Topical Modeling Latent Dirichlet Allocation
bull topic weighted list of words
bull document weighted list of topics
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
New approaches to math-aware similarity clustering andaccessibility
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Tools for automated math extraction from PDF
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Yes you can lthttpeudmlorggt accessible math searchvisibility scalabilityhellip
TEX
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
End of talk overview
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
History of the dream vision of WDML as PubMed 4 Math
In the beginning was vision of all mathematical knowledge peer reviewedverified (100000000 pages) and engineered into one-stop e-shopDL
AMS supported NSF preparation grant (in 2003) for WDMLmdashWorldwidedigital mathematics library planned to be funded by de Moore foundation($100000000 requested) Application was not successful
Publishers started massive digitization themselves
Even other attempts on the European level (FP5 FP6) were not successful
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Vision of European Digital Mathematics Library
Finally three year project or European Digital Mathematics Library EuDML(programme EU CIP-ICT-PSP type Pilot B EU contribution (16 MEur 50of total budget only) February 2010ndashJanuary 2013 The strategy of
Final identity with strapline (stacked)
was
bull to master the technology develop tools and offer them
bull concept of moving wall to motivate and engage commercial publisherswithout Open Access bussiness model
bull to collect data (from existing local or publisherrsquos) digital libraries intolsquoone-stop shoprsquo and achieve critical mass in the domainrarr lsquoa mustmetoorsquo effect then as with PubMed Central
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
EuDML as a virtual library portal
EuDML provides a virtual library based on data from smaller data providersDLs and publishers
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
One portal European Digital Mathematics Library
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Aggregation of data from building bricks of regional repositories
14 data and technology providers plus associated partners as ZMathGoumlttingen libraryhellip
DML content providers serve mostly publisherrsquos or regional more or lessestablished DML repositories The Czech Digital Mathematics LibraryDML-CZ NUMDAM DML-PL DML-PT DML-GR DML-BG DML-EShellip
Aggregation via standard OAI-PMH protocol (OAI servers run by dataproviders)
EuDML metadata schema(s) was borrowed from NLM (heavily funded by USNiH) as it allows also math-awareness (eg math stored both in TEX andMathML) and fully fledged reference lists
Inovation rather than research Example of DML-CZ lthttpdmlczgt with30000+ papers (300000+ pages) For more see (who what browsebrowse similar how to search)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Challenges of Math handling OCR indexing searchhellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Take care ldquoGod is in the detailsrdquo (Mies van der Rohe)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Data heterogenity specificity no free lunch to unify
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Document accessibility 4 DML processing challenges
Conversions (inversion of authoring+typesetting) needed from
born-digital period typesetting by TEX with export of [meta]data into digitallibrary maxTract
retro-digital period scanning geometrical transformations (BookRestorer)OCR (FineReader InftyReader) two-layer PDF
retro-born-digital period not complete tex or dvi data bad formats bitmapfonts of low resolution finally Tesseract
andDocument
Metaminusdata
RETROminusBORN DIGITAL
Image
Extraction
OCR
DataPrinted Analysis
Conversion
RETROminusDIGITISED
BORN DIGITAL
Refinement
Printed
PDFPS
TeX
Image CaptureDocument
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
From PDF to MathML (via LATEX)
Most fulltext available as PDF only often as low quality scanned volumepages Aggregation via IP protected OAI-PMH including the PDFs behingmoving wall
Workflow based in the case of
born-digital PDFs on maxTract otherwise on PDFBox (plain text)
bitmap PDFs on Infty otherwise on Tesseract (no math)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Infty from Fukuoka
Run in parallel in Brno Grenoble and Lisbon to speed up Almost 200Kpapers (more than 1M pages and still running)
Working with prof Suzuki to improve further (automation support forRussian LATEX driverhellip)
Automated only no time (and money) to fix OCR errors
MathML output used for [internal] indexing and similarity computations onlynot for metadata or export
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham II adding accessibility
Adding accessibility to mathematical documents on multiple levels
bull access to content for print impaired users such as those with visualimpairments dyslexia or dyspraxia
bull output compatible with web browsers screen readers and tools such ascopy and paste which is achieved by enriching the regular text withmathematical markup The output can also be used directly within thelimits of the presentation MathML produced as machine readablemathematical input to software systems such as Mathematica or Maple
On EuDML 10k+ fulltexts are served mostly for reading in Chrome (HTML5output) andor Adobe Acrobat Reader (as multiple-layer PDFs [no taggedPDFs yet])
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Metadata and conversions MathML and LATEX
Data heterogenity plethora of formats validation and conversions
world of authors LATEX TEX notation of mathematics
world of applicationsdata exchange XML MathML
REPOX engine (by IST Lisbon) to remap different metadata formats to uniquerepresentation
Metadata on the webmdashW3C standards MathML WAI-ARIA (WebAccessibility InitiativemdashAcessible Rich Internet Applications) WCAG (WebContent Accessibility Guidelines) 20
Big volumes rarr high automation to save costs converting to MathML (viaTralics) to allow discoverability and indexing (formulae similarity search)130+K fulltexts with MathML Infty still runninghellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Search
Vast amounts of [moving] contents in digital libraries from browsing tosearch from static links to indirect search links or even semantic search
Searching is crucial part of accessibility and exploration of the great ideasaround carved into 0s and 1s
Pragmatic decisions on math indexing level presentation vs content vssemantic In EuDML first step scalable presentation (structural) withmethods (tree indexing and weighting) extendable for content or semantic
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Math Search (MIR)
A picture is worth thousands words
ldquoA math formulae is worth of hundreds of wordsldquo (Ross Moore)
There are papers with more formulae than plain text
Precision vs Recall optimisation optimizing recall is better for exploratorysearching (we have not opted for precision as holy grail at the moment)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Motivation for MSE (including formulae) ndash cont
prof James Davenport CEIC member MKM2011 PC chair on panel atEuDML workshop in Bertinoro as a reply to the question ldquowhat functionalityand incentives would made a working mathematician to login and use amodern DML as EuDMLrdquo
ldquoMath formulae searchrdquo
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why math search is more relevant now than ever
bull Allowing formulas in queries helps to disambiguate and narrow searchSometimes the only difference among set of notionskey words would bein a math formula
bull Example 1 knowing the solution of partial differential equation in L1(C3)is there one in L2(C5)
bull Example 2 historians may want to follow the history of a (class of) formula(s)across languages and vocabularies (eg same objects studiedused byphysicists and mathematicians under different names)
bull Imagine your favourite ebook math textbook being [TEX]-search awaremdashegyour search app supports math formulae search
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
We did not start from scratch
Compare googlecomsearchq=Einstein with math-awaresearch of Einstein+$E=mcˆ2$ over arXiv
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Existing systems ndash pros and cons
bull MathDex formerly MathFind seven digit figure NSF grant by Design Science(Robert Miner) Lucene based indexing n-grams of presentation MathML pioneering conversion effort
bull EgoMath and EgoMath2 based on full text web search system Egothor presentation MathML for indexing idea of formulae augmentation α-equivalencealgorithms and relevance calculation
bull LATEXSearch MSE offered by Springer closed source only for LATEX math stringapproximate match based on strings no formulae structure matching smalldatabase 3 million formulae from lsquorandomrsquo sources
bull LeActiveMath indexing string tokens from OMDoc with OpenMath semanticnotation only for documents authored for LeActiveMath learning environment
bull DLMF only for documents authored for DLMF in special markup equation search
bull MathWeb Search semantic approach ndash uses substitution trees ndash not based on full
text searching supports Content MathML and OpenMath problem with acquiring
semantic data
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MIaS mdash Math Indexer and Searcher
bull math-aware full-text based search engine
bull joins textual and mathematical querying
bull MathML or TEX input
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MSE overall design
input document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
math
searching
indexing
Lucene Core
canonicalization
mat
h
Preprocessing into
canonicalized presentation
MathML
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Math indexing design
input canonicalized
document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
mat
h
math
searching
indexing
Lucene
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization canonicalization
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Example
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization
searchingindexing
x y+y3
x y+y3 x y y3 x y 3+
x y+y3 x y y3 x y 3+ id1id 2+id 2
3 id 1id 2 id 1
3
x y+y3 x y y3 x y 3+ id1id 2+id 2
3
id1id 2 id1
3 x y+ yconst yconst id 1id 2+id 2
const id 1const
x y+y3
x y+y2
x y+y2
x y+y 2 id 1id 2+id 2
2
x y+y 2 id1id 2+id 2
2
x y+yconst id 1id 2+id 2
const
x y+yconst id 1id 2+id 2
const Match
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formula processing example ndash subformulae weighting
(a+b2+c 0125)
(a+bc+2 0125)
(a 00875) (+ 00875) (bc+2 00875)
(b 006125) (c+2 006125)
(c 0042875)(+ 0042875)
(2 0042875)
(id 1+2 00343)
(c+const 0030625)
(id 1+const 001715)
(id 1id 2+2 007)
(bc+const 004375)
(id 1id 2+const 0035)
(id 1+id 2id 3+2 01)
(a+bc+const 00625)
(id 1+id 2id 3+const 005)
input
ordering
tokenization
variablesunification
constantsunification
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Implementation
bull Java
bull Lucene 310 now switching to LuceneSolr 4
bull Mathematical part implements Lucenersquos interface Tokenizer ndash able tointegrate to any Lucene based system
bull MIaS4Solr plugin was created for the use in Solr
bull Textual content ndash processed by StandardAnalyzer
bull easily deployable in JavaLucene based system or as a web service
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Search demonstration
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formulae search demonstration comments
Demo web interface httpaurafimunicz8085webmias
bull MathMLTEX input (Tralics [2] for conversion to MathML [])
bull Canonicalization of the query ndash problems with UMCL library [1]
bull Matched document snippet generation
bull MathJax for nicer math rendering and better portability
bull Snuggle TeX for on-the-fly as-you-type rendering
All up and ready on the EuDML system
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Searching (semantically) similar papers
Exploration of a DML browsing (semantically) similar papers
Semantic search via topic modeling Latent Semantic Indexing LatentDirichlet Allocation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Leading Edge Example Automated Meaning Picking from Texts
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Probabilistic Topical Modeling Latent Dirichlet Allocation
bull topic weighted list of words
bull document weighted list of topics
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Tools for automated math extraction from PDF
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Yes you can lthttpeudmlorggt accessible math searchvisibility scalabilityhellip
TEX
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
End of talk overview
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
History of the dream vision of WDML as PubMed 4 Math
In the beginning was vision of all mathematical knowledge peer reviewedverified (100000000 pages) and engineered into one-stop e-shopDL
AMS supported NSF preparation grant (in 2003) for WDMLmdashWorldwidedigital mathematics library planned to be funded by de Moore foundation($100000000 requested) Application was not successful
Publishers started massive digitization themselves
Even other attempts on the European level (FP5 FP6) were not successful
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Vision of European Digital Mathematics Library
Finally three year project or European Digital Mathematics Library EuDML(programme EU CIP-ICT-PSP type Pilot B EU contribution (16 MEur 50of total budget only) February 2010ndashJanuary 2013 The strategy of
Final identity with strapline (stacked)
was
bull to master the technology develop tools and offer them
bull concept of moving wall to motivate and engage commercial publisherswithout Open Access bussiness model
bull to collect data (from existing local or publisherrsquos) digital libraries intolsquoone-stop shoprsquo and achieve critical mass in the domainrarr lsquoa mustmetoorsquo effect then as with PubMed Central
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
EuDML as a virtual library portal
EuDML provides a virtual library based on data from smaller data providersDLs and publishers
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
One portal European Digital Mathematics Library
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Aggregation of data from building bricks of regional repositories
14 data and technology providers plus associated partners as ZMathGoumlttingen libraryhellip
DML content providers serve mostly publisherrsquos or regional more or lessestablished DML repositories The Czech Digital Mathematics LibraryDML-CZ NUMDAM DML-PL DML-PT DML-GR DML-BG DML-EShellip
Aggregation via standard OAI-PMH protocol (OAI servers run by dataproviders)
EuDML metadata schema(s) was borrowed from NLM (heavily funded by USNiH) as it allows also math-awareness (eg math stored both in TEX andMathML) and fully fledged reference lists
Inovation rather than research Example of DML-CZ lthttpdmlczgt with30000+ papers (300000+ pages) For more see (who what browsebrowse similar how to search)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Challenges of Math handling OCR indexing searchhellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Take care ldquoGod is in the detailsrdquo (Mies van der Rohe)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Data heterogenity specificity no free lunch to unify
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Document accessibility 4 DML processing challenges
Conversions (inversion of authoring+typesetting) needed from
born-digital period typesetting by TEX with export of [meta]data into digitallibrary maxTract
retro-digital period scanning geometrical transformations (BookRestorer)OCR (FineReader InftyReader) two-layer PDF
retro-born-digital period not complete tex or dvi data bad formats bitmapfonts of low resolution finally Tesseract
andDocument
Metaminusdata
RETROminusBORN DIGITAL
Image
Extraction
OCR
DataPrinted Analysis
Conversion
RETROminusDIGITISED
BORN DIGITAL
Refinement
Printed
PDFPS
TeX
Image CaptureDocument
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
From PDF to MathML (via LATEX)
Most fulltext available as PDF only often as low quality scanned volumepages Aggregation via IP protected OAI-PMH including the PDFs behingmoving wall
Workflow based in the case of
born-digital PDFs on maxTract otherwise on PDFBox (plain text)
bitmap PDFs on Infty otherwise on Tesseract (no math)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Infty from Fukuoka
Run in parallel in Brno Grenoble and Lisbon to speed up Almost 200Kpapers (more than 1M pages and still running)
Working with prof Suzuki to improve further (automation support forRussian LATEX driverhellip)
Automated only no time (and money) to fix OCR errors
MathML output used for [internal] indexing and similarity computations onlynot for metadata or export
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham II adding accessibility
Adding accessibility to mathematical documents on multiple levels
bull access to content for print impaired users such as those with visualimpairments dyslexia or dyspraxia
bull output compatible with web browsers screen readers and tools such ascopy and paste which is achieved by enriching the regular text withmathematical markup The output can also be used directly within thelimits of the presentation MathML produced as machine readablemathematical input to software systems such as Mathematica or Maple
On EuDML 10k+ fulltexts are served mostly for reading in Chrome (HTML5output) andor Adobe Acrobat Reader (as multiple-layer PDFs [no taggedPDFs yet])
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Metadata and conversions MathML and LATEX
Data heterogenity plethora of formats validation and conversions
world of authors LATEX TEX notation of mathematics
world of applicationsdata exchange XML MathML
REPOX engine (by IST Lisbon) to remap different metadata formats to uniquerepresentation
Metadata on the webmdashW3C standards MathML WAI-ARIA (WebAccessibility InitiativemdashAcessible Rich Internet Applications) WCAG (WebContent Accessibility Guidelines) 20
Big volumes rarr high automation to save costs converting to MathML (viaTralics) to allow discoverability and indexing (formulae similarity search)130+K fulltexts with MathML Infty still runninghellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Search
Vast amounts of [moving] contents in digital libraries from browsing tosearch from static links to indirect search links or even semantic search
Searching is crucial part of accessibility and exploration of the great ideasaround carved into 0s and 1s
Pragmatic decisions on math indexing level presentation vs content vssemantic In EuDML first step scalable presentation (structural) withmethods (tree indexing and weighting) extendable for content or semantic
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Math Search (MIR)
A picture is worth thousands words
ldquoA math formulae is worth of hundreds of wordsldquo (Ross Moore)
There are papers with more formulae than plain text
Precision vs Recall optimisation optimizing recall is better for exploratorysearching (we have not opted for precision as holy grail at the moment)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Motivation for MSE (including formulae) ndash cont
prof James Davenport CEIC member MKM2011 PC chair on panel atEuDML workshop in Bertinoro as a reply to the question ldquowhat functionalityand incentives would made a working mathematician to login and use amodern DML as EuDMLrdquo
ldquoMath formulae searchrdquo
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why math search is more relevant now than ever
bull Allowing formulas in queries helps to disambiguate and narrow searchSometimes the only difference among set of notionskey words would bein a math formula
bull Example 1 knowing the solution of partial differential equation in L1(C3)is there one in L2(C5)
bull Example 2 historians may want to follow the history of a (class of) formula(s)across languages and vocabularies (eg same objects studiedused byphysicists and mathematicians under different names)
bull Imagine your favourite ebook math textbook being [TEX]-search awaremdashegyour search app supports math formulae search
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
We did not start from scratch
Compare googlecomsearchq=Einstein with math-awaresearch of Einstein+$E=mcˆ2$ over arXiv
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Existing systems ndash pros and cons
bull MathDex formerly MathFind seven digit figure NSF grant by Design Science(Robert Miner) Lucene based indexing n-grams of presentation MathML pioneering conversion effort
bull EgoMath and EgoMath2 based on full text web search system Egothor presentation MathML for indexing idea of formulae augmentation α-equivalencealgorithms and relevance calculation
bull LATEXSearch MSE offered by Springer closed source only for LATEX math stringapproximate match based on strings no formulae structure matching smalldatabase 3 million formulae from lsquorandomrsquo sources
bull LeActiveMath indexing string tokens from OMDoc with OpenMath semanticnotation only for documents authored for LeActiveMath learning environment
bull DLMF only for documents authored for DLMF in special markup equation search
bull MathWeb Search semantic approach ndash uses substitution trees ndash not based on full
text searching supports Content MathML and OpenMath problem with acquiring
semantic data
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MIaS mdash Math Indexer and Searcher
bull math-aware full-text based search engine
bull joins textual and mathematical querying
bull MathML or TEX input
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MSE overall design
input document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
math
searching
indexing
Lucene Core
canonicalization
mat
h
Preprocessing into
canonicalized presentation
MathML
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Math indexing design
input canonicalized
document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
mat
h
math
searching
indexing
Lucene
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization canonicalization
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Example
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization
searchingindexing
x y+y3
x y+y3 x y y3 x y 3+
x y+y3 x y y3 x y 3+ id1id 2+id 2
3 id 1id 2 id 1
3
x y+y3 x y y3 x y 3+ id1id 2+id 2
3
id1id 2 id1
3 x y+ yconst yconst id 1id 2+id 2
const id 1const
x y+y3
x y+y2
x y+y2
x y+y 2 id 1id 2+id 2
2
x y+y 2 id1id 2+id 2
2
x y+yconst id 1id 2+id 2
const
x y+yconst id 1id 2+id 2
const Match
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formula processing example ndash subformulae weighting
(a+b2+c 0125)
(a+bc+2 0125)
(a 00875) (+ 00875) (bc+2 00875)
(b 006125) (c+2 006125)
(c 0042875)(+ 0042875)
(2 0042875)
(id 1+2 00343)
(c+const 0030625)
(id 1+const 001715)
(id 1id 2+2 007)
(bc+const 004375)
(id 1id 2+const 0035)
(id 1+id 2id 3+2 01)
(a+bc+const 00625)
(id 1+id 2id 3+const 005)
input
ordering
tokenization
variablesunification
constantsunification
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Implementation
bull Java
bull Lucene 310 now switching to LuceneSolr 4
bull Mathematical part implements Lucenersquos interface Tokenizer ndash able tointegrate to any Lucene based system
bull MIaS4Solr plugin was created for the use in Solr
bull Textual content ndash processed by StandardAnalyzer
bull easily deployable in JavaLucene based system or as a web service
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Search demonstration
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formulae search demonstration comments
Demo web interface httpaurafimunicz8085webmias
bull MathMLTEX input (Tralics [2] for conversion to MathML [])
bull Canonicalization of the query ndash problems with UMCL library [1]
bull Matched document snippet generation
bull MathJax for nicer math rendering and better portability
bull Snuggle TeX for on-the-fly as-you-type rendering
All up and ready on the EuDML system
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Searching (semantically) similar papers
Exploration of a DML browsing (semantically) similar papers
Semantic search via topic modeling Latent Semantic Indexing LatentDirichlet Allocation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Leading Edge Example Automated Meaning Picking from Texts
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Probabilistic Topical Modeling Latent Dirichlet Allocation
bull topic weighted list of words
bull document weighted list of topics
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Yes you can lthttpeudmlorggt accessible math searchvisibility scalabilityhellip
TEX
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
End of talk overview
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
History of the dream vision of WDML as PubMed 4 Math
In the beginning was vision of all mathematical knowledge peer reviewedverified (100000000 pages) and engineered into one-stop e-shopDL
AMS supported NSF preparation grant (in 2003) for WDMLmdashWorldwidedigital mathematics library planned to be funded by de Moore foundation($100000000 requested) Application was not successful
Publishers started massive digitization themselves
Even other attempts on the European level (FP5 FP6) were not successful
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Vision of European Digital Mathematics Library
Finally three year project or European Digital Mathematics Library EuDML(programme EU CIP-ICT-PSP type Pilot B EU contribution (16 MEur 50of total budget only) February 2010ndashJanuary 2013 The strategy of
Final identity with strapline (stacked)
was
bull to master the technology develop tools and offer them
bull concept of moving wall to motivate and engage commercial publisherswithout Open Access bussiness model
bull to collect data (from existing local or publisherrsquos) digital libraries intolsquoone-stop shoprsquo and achieve critical mass in the domainrarr lsquoa mustmetoorsquo effect then as with PubMed Central
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
EuDML as a virtual library portal
EuDML provides a virtual library based on data from smaller data providersDLs and publishers
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
One portal European Digital Mathematics Library
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Aggregation of data from building bricks of regional repositories
14 data and technology providers plus associated partners as ZMathGoumlttingen libraryhellip
DML content providers serve mostly publisherrsquos or regional more or lessestablished DML repositories The Czech Digital Mathematics LibraryDML-CZ NUMDAM DML-PL DML-PT DML-GR DML-BG DML-EShellip
Aggregation via standard OAI-PMH protocol (OAI servers run by dataproviders)
EuDML metadata schema(s) was borrowed from NLM (heavily funded by USNiH) as it allows also math-awareness (eg math stored both in TEX andMathML) and fully fledged reference lists
Inovation rather than research Example of DML-CZ lthttpdmlczgt with30000+ papers (300000+ pages) For more see (who what browsebrowse similar how to search)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Challenges of Math handling OCR indexing searchhellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Take care ldquoGod is in the detailsrdquo (Mies van der Rohe)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Data heterogenity specificity no free lunch to unify
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Document accessibility 4 DML processing challenges
Conversions (inversion of authoring+typesetting) needed from
born-digital period typesetting by TEX with export of [meta]data into digitallibrary maxTract
retro-digital period scanning geometrical transformations (BookRestorer)OCR (FineReader InftyReader) two-layer PDF
retro-born-digital period not complete tex or dvi data bad formats bitmapfonts of low resolution finally Tesseract
andDocument
Metaminusdata
RETROminusBORN DIGITAL
Image
Extraction
OCR
DataPrinted Analysis
Conversion
RETROminusDIGITISED
BORN DIGITAL
Refinement
Printed
PDFPS
TeX
Image CaptureDocument
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
From PDF to MathML (via LATEX)
Most fulltext available as PDF only often as low quality scanned volumepages Aggregation via IP protected OAI-PMH including the PDFs behingmoving wall
Workflow based in the case of
born-digital PDFs on maxTract otherwise on PDFBox (plain text)
bitmap PDFs on Infty otherwise on Tesseract (no math)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Infty from Fukuoka
Run in parallel in Brno Grenoble and Lisbon to speed up Almost 200Kpapers (more than 1M pages and still running)
Working with prof Suzuki to improve further (automation support forRussian LATEX driverhellip)
Automated only no time (and money) to fix OCR errors
MathML output used for [internal] indexing and similarity computations onlynot for metadata or export
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham II adding accessibility
Adding accessibility to mathematical documents on multiple levels
bull access to content for print impaired users such as those with visualimpairments dyslexia or dyspraxia
bull output compatible with web browsers screen readers and tools such ascopy and paste which is achieved by enriching the regular text withmathematical markup The output can also be used directly within thelimits of the presentation MathML produced as machine readablemathematical input to software systems such as Mathematica or Maple
On EuDML 10k+ fulltexts are served mostly for reading in Chrome (HTML5output) andor Adobe Acrobat Reader (as multiple-layer PDFs [no taggedPDFs yet])
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Metadata and conversions MathML and LATEX
Data heterogenity plethora of formats validation and conversions
world of authors LATEX TEX notation of mathematics
world of applicationsdata exchange XML MathML
REPOX engine (by IST Lisbon) to remap different metadata formats to uniquerepresentation
Metadata on the webmdashW3C standards MathML WAI-ARIA (WebAccessibility InitiativemdashAcessible Rich Internet Applications) WCAG (WebContent Accessibility Guidelines) 20
Big volumes rarr high automation to save costs converting to MathML (viaTralics) to allow discoverability and indexing (formulae similarity search)130+K fulltexts with MathML Infty still runninghellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Search
Vast amounts of [moving] contents in digital libraries from browsing tosearch from static links to indirect search links or even semantic search
Searching is crucial part of accessibility and exploration of the great ideasaround carved into 0s and 1s
Pragmatic decisions on math indexing level presentation vs content vssemantic In EuDML first step scalable presentation (structural) withmethods (tree indexing and weighting) extendable for content or semantic
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Math Search (MIR)
A picture is worth thousands words
ldquoA math formulae is worth of hundreds of wordsldquo (Ross Moore)
There are papers with more formulae than plain text
Precision vs Recall optimisation optimizing recall is better for exploratorysearching (we have not opted for precision as holy grail at the moment)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Motivation for MSE (including formulae) ndash cont
prof James Davenport CEIC member MKM2011 PC chair on panel atEuDML workshop in Bertinoro as a reply to the question ldquowhat functionalityand incentives would made a working mathematician to login and use amodern DML as EuDMLrdquo
ldquoMath formulae searchrdquo
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why math search is more relevant now than ever
bull Allowing formulas in queries helps to disambiguate and narrow searchSometimes the only difference among set of notionskey words would bein a math formula
bull Example 1 knowing the solution of partial differential equation in L1(C3)is there one in L2(C5)
bull Example 2 historians may want to follow the history of a (class of) formula(s)across languages and vocabularies (eg same objects studiedused byphysicists and mathematicians under different names)
bull Imagine your favourite ebook math textbook being [TEX]-search awaremdashegyour search app supports math formulae search
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
We did not start from scratch
Compare googlecomsearchq=Einstein with math-awaresearch of Einstein+$E=mcˆ2$ over arXiv
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Existing systems ndash pros and cons
bull MathDex formerly MathFind seven digit figure NSF grant by Design Science(Robert Miner) Lucene based indexing n-grams of presentation MathML pioneering conversion effort
bull EgoMath and EgoMath2 based on full text web search system Egothor presentation MathML for indexing idea of formulae augmentation α-equivalencealgorithms and relevance calculation
bull LATEXSearch MSE offered by Springer closed source only for LATEX math stringapproximate match based on strings no formulae structure matching smalldatabase 3 million formulae from lsquorandomrsquo sources
bull LeActiveMath indexing string tokens from OMDoc with OpenMath semanticnotation only for documents authored for LeActiveMath learning environment
bull DLMF only for documents authored for DLMF in special markup equation search
bull MathWeb Search semantic approach ndash uses substitution trees ndash not based on full
text searching supports Content MathML and OpenMath problem with acquiring
semantic data
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MIaS mdash Math Indexer and Searcher
bull math-aware full-text based search engine
bull joins textual and mathematical querying
bull MathML or TEX input
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MSE overall design
input document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
math
searching
indexing
Lucene Core
canonicalization
mat
h
Preprocessing into
canonicalized presentation
MathML
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Math indexing design
input canonicalized
document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
mat
h
math
searching
indexing
Lucene
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization canonicalization
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Example
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization
searchingindexing
x y+y3
x y+y3 x y y3 x y 3+
x y+y3 x y y3 x y 3+ id1id 2+id 2
3 id 1id 2 id 1
3
x y+y3 x y y3 x y 3+ id1id 2+id 2
3
id1id 2 id1
3 x y+ yconst yconst id 1id 2+id 2
const id 1const
x y+y3
x y+y2
x y+y2
x y+y 2 id 1id 2+id 2
2
x y+y 2 id1id 2+id 2
2
x y+yconst id 1id 2+id 2
const
x y+yconst id 1id 2+id 2
const Match
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formula processing example ndash subformulae weighting
(a+b2+c 0125)
(a+bc+2 0125)
(a 00875) (+ 00875) (bc+2 00875)
(b 006125) (c+2 006125)
(c 0042875)(+ 0042875)
(2 0042875)
(id 1+2 00343)
(c+const 0030625)
(id 1+const 001715)
(id 1id 2+2 007)
(bc+const 004375)
(id 1id 2+const 0035)
(id 1+id 2id 3+2 01)
(a+bc+const 00625)
(id 1+id 2id 3+const 005)
input
ordering
tokenization
variablesunification
constantsunification
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Implementation
bull Java
bull Lucene 310 now switching to LuceneSolr 4
bull Mathematical part implements Lucenersquos interface Tokenizer ndash able tointegrate to any Lucene based system
bull MIaS4Solr plugin was created for the use in Solr
bull Textual content ndash processed by StandardAnalyzer
bull easily deployable in JavaLucene based system or as a web service
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Search demonstration
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formulae search demonstration comments
Demo web interface httpaurafimunicz8085webmias
bull MathMLTEX input (Tralics [2] for conversion to MathML [])
bull Canonicalization of the query ndash problems with UMCL library [1]
bull Matched document snippet generation
bull MathJax for nicer math rendering and better portability
bull Snuggle TeX for on-the-fly as-you-type rendering
All up and ready on the EuDML system
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Searching (semantically) similar papers
Exploration of a DML browsing (semantically) similar papers
Semantic search via topic modeling Latent Semantic Indexing LatentDirichlet Allocation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Leading Edge Example Automated Meaning Picking from Texts
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Probabilistic Topical Modeling Latent Dirichlet Allocation
bull topic weighted list of words
bull document weighted list of topics
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
End of talk overview
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
History of the dream vision of WDML as PubMed 4 Math
In the beginning was vision of all mathematical knowledge peer reviewedverified (100000000 pages) and engineered into one-stop e-shopDL
AMS supported NSF preparation grant (in 2003) for WDMLmdashWorldwidedigital mathematics library planned to be funded by de Moore foundation($100000000 requested) Application was not successful
Publishers started massive digitization themselves
Even other attempts on the European level (FP5 FP6) were not successful
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Vision of European Digital Mathematics Library
Finally three year project or European Digital Mathematics Library EuDML(programme EU CIP-ICT-PSP type Pilot B EU contribution (16 MEur 50of total budget only) February 2010ndashJanuary 2013 The strategy of
Final identity with strapline (stacked)
was
bull to master the technology develop tools and offer them
bull concept of moving wall to motivate and engage commercial publisherswithout Open Access bussiness model
bull to collect data (from existing local or publisherrsquos) digital libraries intolsquoone-stop shoprsquo and achieve critical mass in the domainrarr lsquoa mustmetoorsquo effect then as with PubMed Central
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
EuDML as a virtual library portal
EuDML provides a virtual library based on data from smaller data providersDLs and publishers
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
One portal European Digital Mathematics Library
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Aggregation of data from building bricks of regional repositories
14 data and technology providers plus associated partners as ZMathGoumlttingen libraryhellip
DML content providers serve mostly publisherrsquos or regional more or lessestablished DML repositories The Czech Digital Mathematics LibraryDML-CZ NUMDAM DML-PL DML-PT DML-GR DML-BG DML-EShellip
Aggregation via standard OAI-PMH protocol (OAI servers run by dataproviders)
EuDML metadata schema(s) was borrowed from NLM (heavily funded by USNiH) as it allows also math-awareness (eg math stored both in TEX andMathML) and fully fledged reference lists
Inovation rather than research Example of DML-CZ lthttpdmlczgt with30000+ papers (300000+ pages) For more see (who what browsebrowse similar how to search)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Challenges of Math handling OCR indexing searchhellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Take care ldquoGod is in the detailsrdquo (Mies van der Rohe)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Data heterogenity specificity no free lunch to unify
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Document accessibility 4 DML processing challenges
Conversions (inversion of authoring+typesetting) needed from
born-digital period typesetting by TEX with export of [meta]data into digitallibrary maxTract
retro-digital period scanning geometrical transformations (BookRestorer)OCR (FineReader InftyReader) two-layer PDF
retro-born-digital period not complete tex or dvi data bad formats bitmapfonts of low resolution finally Tesseract
andDocument
Metaminusdata
RETROminusBORN DIGITAL
Image
Extraction
OCR
DataPrinted Analysis
Conversion
RETROminusDIGITISED
BORN DIGITAL
Refinement
Printed
PDFPS
TeX
Image CaptureDocument
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
From PDF to MathML (via LATEX)
Most fulltext available as PDF only often as low quality scanned volumepages Aggregation via IP protected OAI-PMH including the PDFs behingmoving wall
Workflow based in the case of
born-digital PDFs on maxTract otherwise on PDFBox (plain text)
bitmap PDFs on Infty otherwise on Tesseract (no math)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Infty from Fukuoka
Run in parallel in Brno Grenoble and Lisbon to speed up Almost 200Kpapers (more than 1M pages and still running)
Working with prof Suzuki to improve further (automation support forRussian LATEX driverhellip)
Automated only no time (and money) to fix OCR errors
MathML output used for [internal] indexing and similarity computations onlynot for metadata or export
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham II adding accessibility
Adding accessibility to mathematical documents on multiple levels
bull access to content for print impaired users such as those with visualimpairments dyslexia or dyspraxia
bull output compatible with web browsers screen readers and tools such ascopy and paste which is achieved by enriching the regular text withmathematical markup The output can also be used directly within thelimits of the presentation MathML produced as machine readablemathematical input to software systems such as Mathematica or Maple
On EuDML 10k+ fulltexts are served mostly for reading in Chrome (HTML5output) andor Adobe Acrobat Reader (as multiple-layer PDFs [no taggedPDFs yet])
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Metadata and conversions MathML and LATEX
Data heterogenity plethora of formats validation and conversions
world of authors LATEX TEX notation of mathematics
world of applicationsdata exchange XML MathML
REPOX engine (by IST Lisbon) to remap different metadata formats to uniquerepresentation
Metadata on the webmdashW3C standards MathML WAI-ARIA (WebAccessibility InitiativemdashAcessible Rich Internet Applications) WCAG (WebContent Accessibility Guidelines) 20
Big volumes rarr high automation to save costs converting to MathML (viaTralics) to allow discoverability and indexing (formulae similarity search)130+K fulltexts with MathML Infty still runninghellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Search
Vast amounts of [moving] contents in digital libraries from browsing tosearch from static links to indirect search links or even semantic search
Searching is crucial part of accessibility and exploration of the great ideasaround carved into 0s and 1s
Pragmatic decisions on math indexing level presentation vs content vssemantic In EuDML first step scalable presentation (structural) withmethods (tree indexing and weighting) extendable for content or semantic
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Math Search (MIR)
A picture is worth thousands words
ldquoA math formulae is worth of hundreds of wordsldquo (Ross Moore)
There are papers with more formulae than plain text
Precision vs Recall optimisation optimizing recall is better for exploratorysearching (we have not opted for precision as holy grail at the moment)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Motivation for MSE (including formulae) ndash cont
prof James Davenport CEIC member MKM2011 PC chair on panel atEuDML workshop in Bertinoro as a reply to the question ldquowhat functionalityand incentives would made a working mathematician to login and use amodern DML as EuDMLrdquo
ldquoMath formulae searchrdquo
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why math search is more relevant now than ever
bull Allowing formulas in queries helps to disambiguate and narrow searchSometimes the only difference among set of notionskey words would bein a math formula
bull Example 1 knowing the solution of partial differential equation in L1(C3)is there one in L2(C5)
bull Example 2 historians may want to follow the history of a (class of) formula(s)across languages and vocabularies (eg same objects studiedused byphysicists and mathematicians under different names)
bull Imagine your favourite ebook math textbook being [TEX]-search awaremdashegyour search app supports math formulae search
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
We did not start from scratch
Compare googlecomsearchq=Einstein with math-awaresearch of Einstein+$E=mcˆ2$ over arXiv
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Existing systems ndash pros and cons
bull MathDex formerly MathFind seven digit figure NSF grant by Design Science(Robert Miner) Lucene based indexing n-grams of presentation MathML pioneering conversion effort
bull EgoMath and EgoMath2 based on full text web search system Egothor presentation MathML for indexing idea of formulae augmentation α-equivalencealgorithms and relevance calculation
bull LATEXSearch MSE offered by Springer closed source only for LATEX math stringapproximate match based on strings no formulae structure matching smalldatabase 3 million formulae from lsquorandomrsquo sources
bull LeActiveMath indexing string tokens from OMDoc with OpenMath semanticnotation only for documents authored for LeActiveMath learning environment
bull DLMF only for documents authored for DLMF in special markup equation search
bull MathWeb Search semantic approach ndash uses substitution trees ndash not based on full
text searching supports Content MathML and OpenMath problem with acquiring
semantic data
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MIaS mdash Math Indexer and Searcher
bull math-aware full-text based search engine
bull joins textual and mathematical querying
bull MathML or TEX input
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MSE overall design
input document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
math
searching
indexing
Lucene Core
canonicalization
mat
h
Preprocessing into
canonicalized presentation
MathML
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Math indexing design
input canonicalized
document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
mat
h
math
searching
indexing
Lucene
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization canonicalization
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Example
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization
searchingindexing
x y+y3
x y+y3 x y y3 x y 3+
x y+y3 x y y3 x y 3+ id1id 2+id 2
3 id 1id 2 id 1
3
x y+y3 x y y3 x y 3+ id1id 2+id 2
3
id1id 2 id1
3 x y+ yconst yconst id 1id 2+id 2
const id 1const
x y+y3
x y+y2
x y+y2
x y+y 2 id 1id 2+id 2
2
x y+y 2 id1id 2+id 2
2
x y+yconst id 1id 2+id 2
const
x y+yconst id 1id 2+id 2
const Match
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formula processing example ndash subformulae weighting
(a+b2+c 0125)
(a+bc+2 0125)
(a 00875) (+ 00875) (bc+2 00875)
(b 006125) (c+2 006125)
(c 0042875)(+ 0042875)
(2 0042875)
(id 1+2 00343)
(c+const 0030625)
(id 1+const 001715)
(id 1id 2+2 007)
(bc+const 004375)
(id 1id 2+const 0035)
(id 1+id 2id 3+2 01)
(a+bc+const 00625)
(id 1+id 2id 3+const 005)
input
ordering
tokenization
variablesunification
constantsunification
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Implementation
bull Java
bull Lucene 310 now switching to LuceneSolr 4
bull Mathematical part implements Lucenersquos interface Tokenizer ndash able tointegrate to any Lucene based system
bull MIaS4Solr plugin was created for the use in Solr
bull Textual content ndash processed by StandardAnalyzer
bull easily deployable in JavaLucene based system or as a web service
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Search demonstration
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formulae search demonstration comments
Demo web interface httpaurafimunicz8085webmias
bull MathMLTEX input (Tralics [2] for conversion to MathML [])
bull Canonicalization of the query ndash problems with UMCL library [1]
bull Matched document snippet generation
bull MathJax for nicer math rendering and better portability
bull Snuggle TeX for on-the-fly as-you-type rendering
All up and ready on the EuDML system
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Searching (semantically) similar papers
Exploration of a DML browsing (semantically) similar papers
Semantic search via topic modeling Latent Semantic Indexing LatentDirichlet Allocation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Leading Edge Example Automated Meaning Picking from Texts
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Probabilistic Topical Modeling Latent Dirichlet Allocation
bull topic weighted list of words
bull document weighted list of topics
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
History of the dream vision of WDML as PubMed 4 Math
In the beginning was vision of all mathematical knowledge peer reviewedverified (100000000 pages) and engineered into one-stop e-shopDL
AMS supported NSF preparation grant (in 2003) for WDMLmdashWorldwidedigital mathematics library planned to be funded by de Moore foundation($100000000 requested) Application was not successful
Publishers started massive digitization themselves
Even other attempts on the European level (FP5 FP6) were not successful
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Vision of European Digital Mathematics Library
Finally three year project or European Digital Mathematics Library EuDML(programme EU CIP-ICT-PSP type Pilot B EU contribution (16 MEur 50of total budget only) February 2010ndashJanuary 2013 The strategy of
Final identity with strapline (stacked)
was
bull to master the technology develop tools and offer them
bull concept of moving wall to motivate and engage commercial publisherswithout Open Access bussiness model
bull to collect data (from existing local or publisherrsquos) digital libraries intolsquoone-stop shoprsquo and achieve critical mass in the domainrarr lsquoa mustmetoorsquo effect then as with PubMed Central
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
EuDML as a virtual library portal
EuDML provides a virtual library based on data from smaller data providersDLs and publishers
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
One portal European Digital Mathematics Library
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Aggregation of data from building bricks of regional repositories
14 data and technology providers plus associated partners as ZMathGoumlttingen libraryhellip
DML content providers serve mostly publisherrsquos or regional more or lessestablished DML repositories The Czech Digital Mathematics LibraryDML-CZ NUMDAM DML-PL DML-PT DML-GR DML-BG DML-EShellip
Aggregation via standard OAI-PMH protocol (OAI servers run by dataproviders)
EuDML metadata schema(s) was borrowed from NLM (heavily funded by USNiH) as it allows also math-awareness (eg math stored both in TEX andMathML) and fully fledged reference lists
Inovation rather than research Example of DML-CZ lthttpdmlczgt with30000+ papers (300000+ pages) For more see (who what browsebrowse similar how to search)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Challenges of Math handling OCR indexing searchhellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Take care ldquoGod is in the detailsrdquo (Mies van der Rohe)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Data heterogenity specificity no free lunch to unify
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Document accessibility 4 DML processing challenges
Conversions (inversion of authoring+typesetting) needed from
born-digital period typesetting by TEX with export of [meta]data into digitallibrary maxTract
retro-digital period scanning geometrical transformations (BookRestorer)OCR (FineReader InftyReader) two-layer PDF
retro-born-digital period not complete tex or dvi data bad formats bitmapfonts of low resolution finally Tesseract
andDocument
Metaminusdata
RETROminusBORN DIGITAL
Image
Extraction
OCR
DataPrinted Analysis
Conversion
RETROminusDIGITISED
BORN DIGITAL
Refinement
Printed
PDFPS
TeX
Image CaptureDocument
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
From PDF to MathML (via LATEX)
Most fulltext available as PDF only often as low quality scanned volumepages Aggregation via IP protected OAI-PMH including the PDFs behingmoving wall
Workflow based in the case of
born-digital PDFs on maxTract otherwise on PDFBox (plain text)
bitmap PDFs on Infty otherwise on Tesseract (no math)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Infty from Fukuoka
Run in parallel in Brno Grenoble and Lisbon to speed up Almost 200Kpapers (more than 1M pages and still running)
Working with prof Suzuki to improve further (automation support forRussian LATEX driverhellip)
Automated only no time (and money) to fix OCR errors
MathML output used for [internal] indexing and similarity computations onlynot for metadata or export
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham II adding accessibility
Adding accessibility to mathematical documents on multiple levels
bull access to content for print impaired users such as those with visualimpairments dyslexia or dyspraxia
bull output compatible with web browsers screen readers and tools such ascopy and paste which is achieved by enriching the regular text withmathematical markup The output can also be used directly within thelimits of the presentation MathML produced as machine readablemathematical input to software systems such as Mathematica or Maple
On EuDML 10k+ fulltexts are served mostly for reading in Chrome (HTML5output) andor Adobe Acrobat Reader (as multiple-layer PDFs [no taggedPDFs yet])
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Metadata and conversions MathML and LATEX
Data heterogenity plethora of formats validation and conversions
world of authors LATEX TEX notation of mathematics
world of applicationsdata exchange XML MathML
REPOX engine (by IST Lisbon) to remap different metadata formats to uniquerepresentation
Metadata on the webmdashW3C standards MathML WAI-ARIA (WebAccessibility InitiativemdashAcessible Rich Internet Applications) WCAG (WebContent Accessibility Guidelines) 20
Big volumes rarr high automation to save costs converting to MathML (viaTralics) to allow discoverability and indexing (formulae similarity search)130+K fulltexts with MathML Infty still runninghellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Search
Vast amounts of [moving] contents in digital libraries from browsing tosearch from static links to indirect search links or even semantic search
Searching is crucial part of accessibility and exploration of the great ideasaround carved into 0s and 1s
Pragmatic decisions on math indexing level presentation vs content vssemantic In EuDML first step scalable presentation (structural) withmethods (tree indexing and weighting) extendable for content or semantic
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Math Search (MIR)
A picture is worth thousands words
ldquoA math formulae is worth of hundreds of wordsldquo (Ross Moore)
There are papers with more formulae than plain text
Precision vs Recall optimisation optimizing recall is better for exploratorysearching (we have not opted for precision as holy grail at the moment)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Motivation for MSE (including formulae) ndash cont
prof James Davenport CEIC member MKM2011 PC chair on panel atEuDML workshop in Bertinoro as a reply to the question ldquowhat functionalityand incentives would made a working mathematician to login and use amodern DML as EuDMLrdquo
ldquoMath formulae searchrdquo
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why math search is more relevant now than ever
bull Allowing formulas in queries helps to disambiguate and narrow searchSometimes the only difference among set of notionskey words would bein a math formula
bull Example 1 knowing the solution of partial differential equation in L1(C3)is there one in L2(C5)
bull Example 2 historians may want to follow the history of a (class of) formula(s)across languages and vocabularies (eg same objects studiedused byphysicists and mathematicians under different names)
bull Imagine your favourite ebook math textbook being [TEX]-search awaremdashegyour search app supports math formulae search
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
We did not start from scratch
Compare googlecomsearchq=Einstein with math-awaresearch of Einstein+$E=mcˆ2$ over arXiv
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Existing systems ndash pros and cons
bull MathDex formerly MathFind seven digit figure NSF grant by Design Science(Robert Miner) Lucene based indexing n-grams of presentation MathML pioneering conversion effort
bull EgoMath and EgoMath2 based on full text web search system Egothor presentation MathML for indexing idea of formulae augmentation α-equivalencealgorithms and relevance calculation
bull LATEXSearch MSE offered by Springer closed source only for LATEX math stringapproximate match based on strings no formulae structure matching smalldatabase 3 million formulae from lsquorandomrsquo sources
bull LeActiveMath indexing string tokens from OMDoc with OpenMath semanticnotation only for documents authored for LeActiveMath learning environment
bull DLMF only for documents authored for DLMF in special markup equation search
bull MathWeb Search semantic approach ndash uses substitution trees ndash not based on full
text searching supports Content MathML and OpenMath problem with acquiring
semantic data
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MIaS mdash Math Indexer and Searcher
bull math-aware full-text based search engine
bull joins textual and mathematical querying
bull MathML or TEX input
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MSE overall design
input document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
math
searching
indexing
Lucene Core
canonicalization
mat
h
Preprocessing into
canonicalized presentation
MathML
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Math indexing design
input canonicalized
document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
mat
h
math
searching
indexing
Lucene
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization canonicalization
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Example
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization
searchingindexing
x y+y3
x y+y3 x y y3 x y 3+
x y+y3 x y y3 x y 3+ id1id 2+id 2
3 id 1id 2 id 1
3
x y+y3 x y y3 x y 3+ id1id 2+id 2
3
id1id 2 id1
3 x y+ yconst yconst id 1id 2+id 2
const id 1const
x y+y3
x y+y2
x y+y2
x y+y 2 id 1id 2+id 2
2
x y+y 2 id1id 2+id 2
2
x y+yconst id 1id 2+id 2
const
x y+yconst id 1id 2+id 2
const Match
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formula processing example ndash subformulae weighting
(a+b2+c 0125)
(a+bc+2 0125)
(a 00875) (+ 00875) (bc+2 00875)
(b 006125) (c+2 006125)
(c 0042875)(+ 0042875)
(2 0042875)
(id 1+2 00343)
(c+const 0030625)
(id 1+const 001715)
(id 1id 2+2 007)
(bc+const 004375)
(id 1id 2+const 0035)
(id 1+id 2id 3+2 01)
(a+bc+const 00625)
(id 1+id 2id 3+const 005)
input
ordering
tokenization
variablesunification
constantsunification
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Implementation
bull Java
bull Lucene 310 now switching to LuceneSolr 4
bull Mathematical part implements Lucenersquos interface Tokenizer ndash able tointegrate to any Lucene based system
bull MIaS4Solr plugin was created for the use in Solr
bull Textual content ndash processed by StandardAnalyzer
bull easily deployable in JavaLucene based system or as a web service
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Search demonstration
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formulae search demonstration comments
Demo web interface httpaurafimunicz8085webmias
bull MathMLTEX input (Tralics [2] for conversion to MathML [])
bull Canonicalization of the query ndash problems with UMCL library [1]
bull Matched document snippet generation
bull MathJax for nicer math rendering and better portability
bull Snuggle TeX for on-the-fly as-you-type rendering
All up and ready on the EuDML system
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Searching (semantically) similar papers
Exploration of a DML browsing (semantically) similar papers
Semantic search via topic modeling Latent Semantic Indexing LatentDirichlet Allocation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Leading Edge Example Automated Meaning Picking from Texts
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Probabilistic Topical Modeling Latent Dirichlet Allocation
bull topic weighted list of words
bull document weighted list of topics
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Vision of European Digital Mathematics Library
Finally three year project or European Digital Mathematics Library EuDML(programme EU CIP-ICT-PSP type Pilot B EU contribution (16 MEur 50of total budget only) February 2010ndashJanuary 2013 The strategy of
Final identity with strapline (stacked)
was
bull to master the technology develop tools and offer them
bull concept of moving wall to motivate and engage commercial publisherswithout Open Access bussiness model
bull to collect data (from existing local or publisherrsquos) digital libraries intolsquoone-stop shoprsquo and achieve critical mass in the domainrarr lsquoa mustmetoorsquo effect then as with PubMed Central
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
EuDML as a virtual library portal
EuDML provides a virtual library based on data from smaller data providersDLs and publishers
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
One portal European Digital Mathematics Library
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Aggregation of data from building bricks of regional repositories
14 data and technology providers plus associated partners as ZMathGoumlttingen libraryhellip
DML content providers serve mostly publisherrsquos or regional more or lessestablished DML repositories The Czech Digital Mathematics LibraryDML-CZ NUMDAM DML-PL DML-PT DML-GR DML-BG DML-EShellip
Aggregation via standard OAI-PMH protocol (OAI servers run by dataproviders)
EuDML metadata schema(s) was borrowed from NLM (heavily funded by USNiH) as it allows also math-awareness (eg math stored both in TEX andMathML) and fully fledged reference lists
Inovation rather than research Example of DML-CZ lthttpdmlczgt with30000+ papers (300000+ pages) For more see (who what browsebrowse similar how to search)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Challenges of Math handling OCR indexing searchhellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Take care ldquoGod is in the detailsrdquo (Mies van der Rohe)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Data heterogenity specificity no free lunch to unify
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Document accessibility 4 DML processing challenges
Conversions (inversion of authoring+typesetting) needed from
born-digital period typesetting by TEX with export of [meta]data into digitallibrary maxTract
retro-digital period scanning geometrical transformations (BookRestorer)OCR (FineReader InftyReader) two-layer PDF
retro-born-digital period not complete tex or dvi data bad formats bitmapfonts of low resolution finally Tesseract
andDocument
Metaminusdata
RETROminusBORN DIGITAL
Image
Extraction
OCR
DataPrinted Analysis
Conversion
RETROminusDIGITISED
BORN DIGITAL
Refinement
Printed
PDFPS
TeX
Image CaptureDocument
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
From PDF to MathML (via LATEX)
Most fulltext available as PDF only often as low quality scanned volumepages Aggregation via IP protected OAI-PMH including the PDFs behingmoving wall
Workflow based in the case of
born-digital PDFs on maxTract otherwise on PDFBox (plain text)
bitmap PDFs on Infty otherwise on Tesseract (no math)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Infty from Fukuoka
Run in parallel in Brno Grenoble and Lisbon to speed up Almost 200Kpapers (more than 1M pages and still running)
Working with prof Suzuki to improve further (automation support forRussian LATEX driverhellip)
Automated only no time (and money) to fix OCR errors
MathML output used for [internal] indexing and similarity computations onlynot for metadata or export
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham II adding accessibility
Adding accessibility to mathematical documents on multiple levels
bull access to content for print impaired users such as those with visualimpairments dyslexia or dyspraxia
bull output compatible with web browsers screen readers and tools such ascopy and paste which is achieved by enriching the regular text withmathematical markup The output can also be used directly within thelimits of the presentation MathML produced as machine readablemathematical input to software systems such as Mathematica or Maple
On EuDML 10k+ fulltexts are served mostly for reading in Chrome (HTML5output) andor Adobe Acrobat Reader (as multiple-layer PDFs [no taggedPDFs yet])
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Metadata and conversions MathML and LATEX
Data heterogenity plethora of formats validation and conversions
world of authors LATEX TEX notation of mathematics
world of applicationsdata exchange XML MathML
REPOX engine (by IST Lisbon) to remap different metadata formats to uniquerepresentation
Metadata on the webmdashW3C standards MathML WAI-ARIA (WebAccessibility InitiativemdashAcessible Rich Internet Applications) WCAG (WebContent Accessibility Guidelines) 20
Big volumes rarr high automation to save costs converting to MathML (viaTralics) to allow discoverability and indexing (formulae similarity search)130+K fulltexts with MathML Infty still runninghellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Search
Vast amounts of [moving] contents in digital libraries from browsing tosearch from static links to indirect search links or even semantic search
Searching is crucial part of accessibility and exploration of the great ideasaround carved into 0s and 1s
Pragmatic decisions on math indexing level presentation vs content vssemantic In EuDML first step scalable presentation (structural) withmethods (tree indexing and weighting) extendable for content or semantic
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Math Search (MIR)
A picture is worth thousands words
ldquoA math formulae is worth of hundreds of wordsldquo (Ross Moore)
There are papers with more formulae than plain text
Precision vs Recall optimisation optimizing recall is better for exploratorysearching (we have not opted for precision as holy grail at the moment)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Motivation for MSE (including formulae) ndash cont
prof James Davenport CEIC member MKM2011 PC chair on panel atEuDML workshop in Bertinoro as a reply to the question ldquowhat functionalityand incentives would made a working mathematician to login and use amodern DML as EuDMLrdquo
ldquoMath formulae searchrdquo
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why math search is more relevant now than ever
bull Allowing formulas in queries helps to disambiguate and narrow searchSometimes the only difference among set of notionskey words would bein a math formula
bull Example 1 knowing the solution of partial differential equation in L1(C3)is there one in L2(C5)
bull Example 2 historians may want to follow the history of a (class of) formula(s)across languages and vocabularies (eg same objects studiedused byphysicists and mathematicians under different names)
bull Imagine your favourite ebook math textbook being [TEX]-search awaremdashegyour search app supports math formulae search
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
We did not start from scratch
Compare googlecomsearchq=Einstein with math-awaresearch of Einstein+$E=mcˆ2$ over arXiv
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Existing systems ndash pros and cons
bull MathDex formerly MathFind seven digit figure NSF grant by Design Science(Robert Miner) Lucene based indexing n-grams of presentation MathML pioneering conversion effort
bull EgoMath and EgoMath2 based on full text web search system Egothor presentation MathML for indexing idea of formulae augmentation α-equivalencealgorithms and relevance calculation
bull LATEXSearch MSE offered by Springer closed source only for LATEX math stringapproximate match based on strings no formulae structure matching smalldatabase 3 million formulae from lsquorandomrsquo sources
bull LeActiveMath indexing string tokens from OMDoc with OpenMath semanticnotation only for documents authored for LeActiveMath learning environment
bull DLMF only for documents authored for DLMF in special markup equation search
bull MathWeb Search semantic approach ndash uses substitution trees ndash not based on full
text searching supports Content MathML and OpenMath problem with acquiring
semantic data
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MIaS mdash Math Indexer and Searcher
bull math-aware full-text based search engine
bull joins textual and mathematical querying
bull MathML or TEX input
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MSE overall design
input document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
math
searching
indexing
Lucene Core
canonicalization
mat
h
Preprocessing into
canonicalized presentation
MathML
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Math indexing design
input canonicalized
document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
mat
h
math
searching
indexing
Lucene
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization canonicalization
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Example
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization
searchingindexing
x y+y3
x y+y3 x y y3 x y 3+
x y+y3 x y y3 x y 3+ id1id 2+id 2
3 id 1id 2 id 1
3
x y+y3 x y y3 x y 3+ id1id 2+id 2
3
id1id 2 id1
3 x y+ yconst yconst id 1id 2+id 2
const id 1const
x y+y3
x y+y2
x y+y2
x y+y 2 id 1id 2+id 2
2
x y+y 2 id1id 2+id 2
2
x y+yconst id 1id 2+id 2
const
x y+yconst id 1id 2+id 2
const Match
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formula processing example ndash subformulae weighting
(a+b2+c 0125)
(a+bc+2 0125)
(a 00875) (+ 00875) (bc+2 00875)
(b 006125) (c+2 006125)
(c 0042875)(+ 0042875)
(2 0042875)
(id 1+2 00343)
(c+const 0030625)
(id 1+const 001715)
(id 1id 2+2 007)
(bc+const 004375)
(id 1id 2+const 0035)
(id 1+id 2id 3+2 01)
(a+bc+const 00625)
(id 1+id 2id 3+const 005)
input
ordering
tokenization
variablesunification
constantsunification
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Implementation
bull Java
bull Lucene 310 now switching to LuceneSolr 4
bull Mathematical part implements Lucenersquos interface Tokenizer ndash able tointegrate to any Lucene based system
bull MIaS4Solr plugin was created for the use in Solr
bull Textual content ndash processed by StandardAnalyzer
bull easily deployable in JavaLucene based system or as a web service
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Search demonstration
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formulae search demonstration comments
Demo web interface httpaurafimunicz8085webmias
bull MathMLTEX input (Tralics [2] for conversion to MathML [])
bull Canonicalization of the query ndash problems with UMCL library [1]
bull Matched document snippet generation
bull MathJax for nicer math rendering and better portability
bull Snuggle TeX for on-the-fly as-you-type rendering
All up and ready on the EuDML system
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Searching (semantically) similar papers
Exploration of a DML browsing (semantically) similar papers
Semantic search via topic modeling Latent Semantic Indexing LatentDirichlet Allocation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Leading Edge Example Automated Meaning Picking from Texts
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Probabilistic Topical Modeling Latent Dirichlet Allocation
bull topic weighted list of words
bull document weighted list of topics
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
EuDML as a virtual library portal
EuDML provides a virtual library based on data from smaller data providersDLs and publishers
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
One portal European Digital Mathematics Library
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Aggregation of data from building bricks of regional repositories
14 data and technology providers plus associated partners as ZMathGoumlttingen libraryhellip
DML content providers serve mostly publisherrsquos or regional more or lessestablished DML repositories The Czech Digital Mathematics LibraryDML-CZ NUMDAM DML-PL DML-PT DML-GR DML-BG DML-EShellip
Aggregation via standard OAI-PMH protocol (OAI servers run by dataproviders)
EuDML metadata schema(s) was borrowed from NLM (heavily funded by USNiH) as it allows also math-awareness (eg math stored both in TEX andMathML) and fully fledged reference lists
Inovation rather than research Example of DML-CZ lthttpdmlczgt with30000+ papers (300000+ pages) For more see (who what browsebrowse similar how to search)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Challenges of Math handling OCR indexing searchhellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Take care ldquoGod is in the detailsrdquo (Mies van der Rohe)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Data heterogenity specificity no free lunch to unify
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Document accessibility 4 DML processing challenges
Conversions (inversion of authoring+typesetting) needed from
born-digital period typesetting by TEX with export of [meta]data into digitallibrary maxTract
retro-digital period scanning geometrical transformations (BookRestorer)OCR (FineReader InftyReader) two-layer PDF
retro-born-digital period not complete tex or dvi data bad formats bitmapfonts of low resolution finally Tesseract
andDocument
Metaminusdata
RETROminusBORN DIGITAL
Image
Extraction
OCR
DataPrinted Analysis
Conversion
RETROminusDIGITISED
BORN DIGITAL
Refinement
Printed
PDFPS
TeX
Image CaptureDocument
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
From PDF to MathML (via LATEX)
Most fulltext available as PDF only often as low quality scanned volumepages Aggregation via IP protected OAI-PMH including the PDFs behingmoving wall
Workflow based in the case of
born-digital PDFs on maxTract otherwise on PDFBox (plain text)
bitmap PDFs on Infty otherwise on Tesseract (no math)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Infty from Fukuoka
Run in parallel in Brno Grenoble and Lisbon to speed up Almost 200Kpapers (more than 1M pages and still running)
Working with prof Suzuki to improve further (automation support forRussian LATEX driverhellip)
Automated only no time (and money) to fix OCR errors
MathML output used for [internal] indexing and similarity computations onlynot for metadata or export
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham II adding accessibility
Adding accessibility to mathematical documents on multiple levels
bull access to content for print impaired users such as those with visualimpairments dyslexia or dyspraxia
bull output compatible with web browsers screen readers and tools such ascopy and paste which is achieved by enriching the regular text withmathematical markup The output can also be used directly within thelimits of the presentation MathML produced as machine readablemathematical input to software systems such as Mathematica or Maple
On EuDML 10k+ fulltexts are served mostly for reading in Chrome (HTML5output) andor Adobe Acrobat Reader (as multiple-layer PDFs [no taggedPDFs yet])
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Metadata and conversions MathML and LATEX
Data heterogenity plethora of formats validation and conversions
world of authors LATEX TEX notation of mathematics
world of applicationsdata exchange XML MathML
REPOX engine (by IST Lisbon) to remap different metadata formats to uniquerepresentation
Metadata on the webmdashW3C standards MathML WAI-ARIA (WebAccessibility InitiativemdashAcessible Rich Internet Applications) WCAG (WebContent Accessibility Guidelines) 20
Big volumes rarr high automation to save costs converting to MathML (viaTralics) to allow discoverability and indexing (formulae similarity search)130+K fulltexts with MathML Infty still runninghellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Search
Vast amounts of [moving] contents in digital libraries from browsing tosearch from static links to indirect search links or even semantic search
Searching is crucial part of accessibility and exploration of the great ideasaround carved into 0s and 1s
Pragmatic decisions on math indexing level presentation vs content vssemantic In EuDML first step scalable presentation (structural) withmethods (tree indexing and weighting) extendable for content or semantic
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Math Search (MIR)
A picture is worth thousands words
ldquoA math formulae is worth of hundreds of wordsldquo (Ross Moore)
There are papers with more formulae than plain text
Precision vs Recall optimisation optimizing recall is better for exploratorysearching (we have not opted for precision as holy grail at the moment)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Motivation for MSE (including formulae) ndash cont
prof James Davenport CEIC member MKM2011 PC chair on panel atEuDML workshop in Bertinoro as a reply to the question ldquowhat functionalityand incentives would made a working mathematician to login and use amodern DML as EuDMLrdquo
ldquoMath formulae searchrdquo
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why math search is more relevant now than ever
bull Allowing formulas in queries helps to disambiguate and narrow searchSometimes the only difference among set of notionskey words would bein a math formula
bull Example 1 knowing the solution of partial differential equation in L1(C3)is there one in L2(C5)
bull Example 2 historians may want to follow the history of a (class of) formula(s)across languages and vocabularies (eg same objects studiedused byphysicists and mathematicians under different names)
bull Imagine your favourite ebook math textbook being [TEX]-search awaremdashegyour search app supports math formulae search
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
We did not start from scratch
Compare googlecomsearchq=Einstein with math-awaresearch of Einstein+$E=mcˆ2$ over arXiv
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Existing systems ndash pros and cons
bull MathDex formerly MathFind seven digit figure NSF grant by Design Science(Robert Miner) Lucene based indexing n-grams of presentation MathML pioneering conversion effort
bull EgoMath and EgoMath2 based on full text web search system Egothor presentation MathML for indexing idea of formulae augmentation α-equivalencealgorithms and relevance calculation
bull LATEXSearch MSE offered by Springer closed source only for LATEX math stringapproximate match based on strings no formulae structure matching smalldatabase 3 million formulae from lsquorandomrsquo sources
bull LeActiveMath indexing string tokens from OMDoc with OpenMath semanticnotation only for documents authored for LeActiveMath learning environment
bull DLMF only for documents authored for DLMF in special markup equation search
bull MathWeb Search semantic approach ndash uses substitution trees ndash not based on full
text searching supports Content MathML and OpenMath problem with acquiring
semantic data
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MIaS mdash Math Indexer and Searcher
bull math-aware full-text based search engine
bull joins textual and mathematical querying
bull MathML or TEX input
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MSE overall design
input document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
math
searching
indexing
Lucene Core
canonicalization
mat
h
Preprocessing into
canonicalized presentation
MathML
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Math indexing design
input canonicalized
document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
mat
h
math
searching
indexing
Lucene
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization canonicalization
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Example
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization
searchingindexing
x y+y3
x y+y3 x y y3 x y 3+
x y+y3 x y y3 x y 3+ id1id 2+id 2
3 id 1id 2 id 1
3
x y+y3 x y y3 x y 3+ id1id 2+id 2
3
id1id 2 id1
3 x y+ yconst yconst id 1id 2+id 2
const id 1const
x y+y3
x y+y2
x y+y2
x y+y 2 id 1id 2+id 2
2
x y+y 2 id1id 2+id 2
2
x y+yconst id 1id 2+id 2
const
x y+yconst id 1id 2+id 2
const Match
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formula processing example ndash subformulae weighting
(a+b2+c 0125)
(a+bc+2 0125)
(a 00875) (+ 00875) (bc+2 00875)
(b 006125) (c+2 006125)
(c 0042875)(+ 0042875)
(2 0042875)
(id 1+2 00343)
(c+const 0030625)
(id 1+const 001715)
(id 1id 2+2 007)
(bc+const 004375)
(id 1id 2+const 0035)
(id 1+id 2id 3+2 01)
(a+bc+const 00625)
(id 1+id 2id 3+const 005)
input
ordering
tokenization
variablesunification
constantsunification
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Implementation
bull Java
bull Lucene 310 now switching to LuceneSolr 4
bull Mathematical part implements Lucenersquos interface Tokenizer ndash able tointegrate to any Lucene based system
bull MIaS4Solr plugin was created for the use in Solr
bull Textual content ndash processed by StandardAnalyzer
bull easily deployable in JavaLucene based system or as a web service
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Search demonstration
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formulae search demonstration comments
Demo web interface httpaurafimunicz8085webmias
bull MathMLTEX input (Tralics [2] for conversion to MathML [])
bull Canonicalization of the query ndash problems with UMCL library [1]
bull Matched document snippet generation
bull MathJax for nicer math rendering and better portability
bull Snuggle TeX for on-the-fly as-you-type rendering
All up and ready on the EuDML system
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Searching (semantically) similar papers
Exploration of a DML browsing (semantically) similar papers
Semantic search via topic modeling Latent Semantic Indexing LatentDirichlet Allocation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Leading Edge Example Automated Meaning Picking from Texts
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Probabilistic Topical Modeling Latent Dirichlet Allocation
bull topic weighted list of words
bull document weighted list of topics
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
One portal European Digital Mathematics Library
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Aggregation of data from building bricks of regional repositories
14 data and technology providers plus associated partners as ZMathGoumlttingen libraryhellip
DML content providers serve mostly publisherrsquos or regional more or lessestablished DML repositories The Czech Digital Mathematics LibraryDML-CZ NUMDAM DML-PL DML-PT DML-GR DML-BG DML-EShellip
Aggregation via standard OAI-PMH protocol (OAI servers run by dataproviders)
EuDML metadata schema(s) was borrowed from NLM (heavily funded by USNiH) as it allows also math-awareness (eg math stored both in TEX andMathML) and fully fledged reference lists
Inovation rather than research Example of DML-CZ lthttpdmlczgt with30000+ papers (300000+ pages) For more see (who what browsebrowse similar how to search)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Challenges of Math handling OCR indexing searchhellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Take care ldquoGod is in the detailsrdquo (Mies van der Rohe)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Data heterogenity specificity no free lunch to unify
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Document accessibility 4 DML processing challenges
Conversions (inversion of authoring+typesetting) needed from
born-digital period typesetting by TEX with export of [meta]data into digitallibrary maxTract
retro-digital period scanning geometrical transformations (BookRestorer)OCR (FineReader InftyReader) two-layer PDF
retro-born-digital period not complete tex or dvi data bad formats bitmapfonts of low resolution finally Tesseract
andDocument
Metaminusdata
RETROminusBORN DIGITAL
Image
Extraction
OCR
DataPrinted Analysis
Conversion
RETROminusDIGITISED
BORN DIGITAL
Refinement
Printed
PDFPS
TeX
Image CaptureDocument
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
From PDF to MathML (via LATEX)
Most fulltext available as PDF only often as low quality scanned volumepages Aggregation via IP protected OAI-PMH including the PDFs behingmoving wall
Workflow based in the case of
born-digital PDFs on maxTract otherwise on PDFBox (plain text)
bitmap PDFs on Infty otherwise on Tesseract (no math)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Infty from Fukuoka
Run in parallel in Brno Grenoble and Lisbon to speed up Almost 200Kpapers (more than 1M pages and still running)
Working with prof Suzuki to improve further (automation support forRussian LATEX driverhellip)
Automated only no time (and money) to fix OCR errors
MathML output used for [internal] indexing and similarity computations onlynot for metadata or export
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham II adding accessibility
Adding accessibility to mathematical documents on multiple levels
bull access to content for print impaired users such as those with visualimpairments dyslexia or dyspraxia
bull output compatible with web browsers screen readers and tools such ascopy and paste which is achieved by enriching the regular text withmathematical markup The output can also be used directly within thelimits of the presentation MathML produced as machine readablemathematical input to software systems such as Mathematica or Maple
On EuDML 10k+ fulltexts are served mostly for reading in Chrome (HTML5output) andor Adobe Acrobat Reader (as multiple-layer PDFs [no taggedPDFs yet])
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Metadata and conversions MathML and LATEX
Data heterogenity plethora of formats validation and conversions
world of authors LATEX TEX notation of mathematics
world of applicationsdata exchange XML MathML
REPOX engine (by IST Lisbon) to remap different metadata formats to uniquerepresentation
Metadata on the webmdashW3C standards MathML WAI-ARIA (WebAccessibility InitiativemdashAcessible Rich Internet Applications) WCAG (WebContent Accessibility Guidelines) 20
Big volumes rarr high automation to save costs converting to MathML (viaTralics) to allow discoverability and indexing (formulae similarity search)130+K fulltexts with MathML Infty still runninghellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Search
Vast amounts of [moving] contents in digital libraries from browsing tosearch from static links to indirect search links or even semantic search
Searching is crucial part of accessibility and exploration of the great ideasaround carved into 0s and 1s
Pragmatic decisions on math indexing level presentation vs content vssemantic In EuDML first step scalable presentation (structural) withmethods (tree indexing and weighting) extendable for content or semantic
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Math Search (MIR)
A picture is worth thousands words
ldquoA math formulae is worth of hundreds of wordsldquo (Ross Moore)
There are papers with more formulae than plain text
Precision vs Recall optimisation optimizing recall is better for exploratorysearching (we have not opted for precision as holy grail at the moment)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Motivation for MSE (including formulae) ndash cont
prof James Davenport CEIC member MKM2011 PC chair on panel atEuDML workshop in Bertinoro as a reply to the question ldquowhat functionalityand incentives would made a working mathematician to login and use amodern DML as EuDMLrdquo
ldquoMath formulae searchrdquo
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why math search is more relevant now than ever
bull Allowing formulas in queries helps to disambiguate and narrow searchSometimes the only difference among set of notionskey words would bein a math formula
bull Example 1 knowing the solution of partial differential equation in L1(C3)is there one in L2(C5)
bull Example 2 historians may want to follow the history of a (class of) formula(s)across languages and vocabularies (eg same objects studiedused byphysicists and mathematicians under different names)
bull Imagine your favourite ebook math textbook being [TEX]-search awaremdashegyour search app supports math formulae search
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
We did not start from scratch
Compare googlecomsearchq=Einstein with math-awaresearch of Einstein+$E=mcˆ2$ over arXiv
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Existing systems ndash pros and cons
bull MathDex formerly MathFind seven digit figure NSF grant by Design Science(Robert Miner) Lucene based indexing n-grams of presentation MathML pioneering conversion effort
bull EgoMath and EgoMath2 based on full text web search system Egothor presentation MathML for indexing idea of formulae augmentation α-equivalencealgorithms and relevance calculation
bull LATEXSearch MSE offered by Springer closed source only for LATEX math stringapproximate match based on strings no formulae structure matching smalldatabase 3 million formulae from lsquorandomrsquo sources
bull LeActiveMath indexing string tokens from OMDoc with OpenMath semanticnotation only for documents authored for LeActiveMath learning environment
bull DLMF only for documents authored for DLMF in special markup equation search
bull MathWeb Search semantic approach ndash uses substitution trees ndash not based on full
text searching supports Content MathML and OpenMath problem with acquiring
semantic data
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MIaS mdash Math Indexer and Searcher
bull math-aware full-text based search engine
bull joins textual and mathematical querying
bull MathML or TEX input
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MSE overall design
input document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
math
searching
indexing
Lucene Core
canonicalization
mat
h
Preprocessing into
canonicalized presentation
MathML
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Math indexing design
input canonicalized
document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
mat
h
math
searching
indexing
Lucene
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization canonicalization
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Example
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization
searchingindexing
x y+y3
x y+y3 x y y3 x y 3+
x y+y3 x y y3 x y 3+ id1id 2+id 2
3 id 1id 2 id 1
3
x y+y3 x y y3 x y 3+ id1id 2+id 2
3
id1id 2 id1
3 x y+ yconst yconst id 1id 2+id 2
const id 1const
x y+y3
x y+y2
x y+y2
x y+y 2 id 1id 2+id 2
2
x y+y 2 id1id 2+id 2
2
x y+yconst id 1id 2+id 2
const
x y+yconst id 1id 2+id 2
const Match
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formula processing example ndash subformulae weighting
(a+b2+c 0125)
(a+bc+2 0125)
(a 00875) (+ 00875) (bc+2 00875)
(b 006125) (c+2 006125)
(c 0042875)(+ 0042875)
(2 0042875)
(id 1+2 00343)
(c+const 0030625)
(id 1+const 001715)
(id 1id 2+2 007)
(bc+const 004375)
(id 1id 2+const 0035)
(id 1+id 2id 3+2 01)
(a+bc+const 00625)
(id 1+id 2id 3+const 005)
input
ordering
tokenization
variablesunification
constantsunification
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Implementation
bull Java
bull Lucene 310 now switching to LuceneSolr 4
bull Mathematical part implements Lucenersquos interface Tokenizer ndash able tointegrate to any Lucene based system
bull MIaS4Solr plugin was created for the use in Solr
bull Textual content ndash processed by StandardAnalyzer
bull easily deployable in JavaLucene based system or as a web service
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Search demonstration
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formulae search demonstration comments
Demo web interface httpaurafimunicz8085webmias
bull MathMLTEX input (Tralics [2] for conversion to MathML [])
bull Canonicalization of the query ndash problems with UMCL library [1]
bull Matched document snippet generation
bull MathJax for nicer math rendering and better portability
bull Snuggle TeX for on-the-fly as-you-type rendering
All up and ready on the EuDML system
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Searching (semantically) similar papers
Exploration of a DML browsing (semantically) similar papers
Semantic search via topic modeling Latent Semantic Indexing LatentDirichlet Allocation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Leading Edge Example Automated Meaning Picking from Texts
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Probabilistic Topical Modeling Latent Dirichlet Allocation
bull topic weighted list of words
bull document weighted list of topics
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Aggregation of data from building bricks of regional repositories
14 data and technology providers plus associated partners as ZMathGoumlttingen libraryhellip
DML content providers serve mostly publisherrsquos or regional more or lessestablished DML repositories The Czech Digital Mathematics LibraryDML-CZ NUMDAM DML-PL DML-PT DML-GR DML-BG DML-EShellip
Aggregation via standard OAI-PMH protocol (OAI servers run by dataproviders)
EuDML metadata schema(s) was borrowed from NLM (heavily funded by USNiH) as it allows also math-awareness (eg math stored both in TEX andMathML) and fully fledged reference lists
Inovation rather than research Example of DML-CZ lthttpdmlczgt with30000+ papers (300000+ pages) For more see (who what browsebrowse similar how to search)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Challenges of Math handling OCR indexing searchhellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Take care ldquoGod is in the detailsrdquo (Mies van der Rohe)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Data heterogenity specificity no free lunch to unify
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Document accessibility 4 DML processing challenges
Conversions (inversion of authoring+typesetting) needed from
born-digital period typesetting by TEX with export of [meta]data into digitallibrary maxTract
retro-digital period scanning geometrical transformations (BookRestorer)OCR (FineReader InftyReader) two-layer PDF
retro-born-digital period not complete tex or dvi data bad formats bitmapfonts of low resolution finally Tesseract
andDocument
Metaminusdata
RETROminusBORN DIGITAL
Image
Extraction
OCR
DataPrinted Analysis
Conversion
RETROminusDIGITISED
BORN DIGITAL
Refinement
Printed
PDFPS
TeX
Image CaptureDocument
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
From PDF to MathML (via LATEX)
Most fulltext available as PDF only often as low quality scanned volumepages Aggregation via IP protected OAI-PMH including the PDFs behingmoving wall
Workflow based in the case of
born-digital PDFs on maxTract otherwise on PDFBox (plain text)
bitmap PDFs on Infty otherwise on Tesseract (no math)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Infty from Fukuoka
Run in parallel in Brno Grenoble and Lisbon to speed up Almost 200Kpapers (more than 1M pages and still running)
Working with prof Suzuki to improve further (automation support forRussian LATEX driverhellip)
Automated only no time (and money) to fix OCR errors
MathML output used for [internal] indexing and similarity computations onlynot for metadata or export
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham II adding accessibility
Adding accessibility to mathematical documents on multiple levels
bull access to content for print impaired users such as those with visualimpairments dyslexia or dyspraxia
bull output compatible with web browsers screen readers and tools such ascopy and paste which is achieved by enriching the regular text withmathematical markup The output can also be used directly within thelimits of the presentation MathML produced as machine readablemathematical input to software systems such as Mathematica or Maple
On EuDML 10k+ fulltexts are served mostly for reading in Chrome (HTML5output) andor Adobe Acrobat Reader (as multiple-layer PDFs [no taggedPDFs yet])
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Metadata and conversions MathML and LATEX
Data heterogenity plethora of formats validation and conversions
world of authors LATEX TEX notation of mathematics
world of applicationsdata exchange XML MathML
REPOX engine (by IST Lisbon) to remap different metadata formats to uniquerepresentation
Metadata on the webmdashW3C standards MathML WAI-ARIA (WebAccessibility InitiativemdashAcessible Rich Internet Applications) WCAG (WebContent Accessibility Guidelines) 20
Big volumes rarr high automation to save costs converting to MathML (viaTralics) to allow discoverability and indexing (formulae similarity search)130+K fulltexts with MathML Infty still runninghellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Search
Vast amounts of [moving] contents in digital libraries from browsing tosearch from static links to indirect search links or even semantic search
Searching is crucial part of accessibility and exploration of the great ideasaround carved into 0s and 1s
Pragmatic decisions on math indexing level presentation vs content vssemantic In EuDML first step scalable presentation (structural) withmethods (tree indexing and weighting) extendable for content or semantic
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Math Search (MIR)
A picture is worth thousands words
ldquoA math formulae is worth of hundreds of wordsldquo (Ross Moore)
There are papers with more formulae than plain text
Precision vs Recall optimisation optimizing recall is better for exploratorysearching (we have not opted for precision as holy grail at the moment)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Motivation for MSE (including formulae) ndash cont
prof James Davenport CEIC member MKM2011 PC chair on panel atEuDML workshop in Bertinoro as a reply to the question ldquowhat functionalityand incentives would made a working mathematician to login and use amodern DML as EuDMLrdquo
ldquoMath formulae searchrdquo
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why math search is more relevant now than ever
bull Allowing formulas in queries helps to disambiguate and narrow searchSometimes the only difference among set of notionskey words would bein a math formula
bull Example 1 knowing the solution of partial differential equation in L1(C3)is there one in L2(C5)
bull Example 2 historians may want to follow the history of a (class of) formula(s)across languages and vocabularies (eg same objects studiedused byphysicists and mathematicians under different names)
bull Imagine your favourite ebook math textbook being [TEX]-search awaremdashegyour search app supports math formulae search
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
We did not start from scratch
Compare googlecomsearchq=Einstein with math-awaresearch of Einstein+$E=mcˆ2$ over arXiv
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Existing systems ndash pros and cons
bull MathDex formerly MathFind seven digit figure NSF grant by Design Science(Robert Miner) Lucene based indexing n-grams of presentation MathML pioneering conversion effort
bull EgoMath and EgoMath2 based on full text web search system Egothor presentation MathML for indexing idea of formulae augmentation α-equivalencealgorithms and relevance calculation
bull LATEXSearch MSE offered by Springer closed source only for LATEX math stringapproximate match based on strings no formulae structure matching smalldatabase 3 million formulae from lsquorandomrsquo sources
bull LeActiveMath indexing string tokens from OMDoc with OpenMath semanticnotation only for documents authored for LeActiveMath learning environment
bull DLMF only for documents authored for DLMF in special markup equation search
bull MathWeb Search semantic approach ndash uses substitution trees ndash not based on full
text searching supports Content MathML and OpenMath problem with acquiring
semantic data
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MIaS mdash Math Indexer and Searcher
bull math-aware full-text based search engine
bull joins textual and mathematical querying
bull MathML or TEX input
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MSE overall design
input document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
math
searching
indexing
Lucene Core
canonicalization
mat
h
Preprocessing into
canonicalized presentation
MathML
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Math indexing design
input canonicalized
document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
mat
h
math
searching
indexing
Lucene
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization canonicalization
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Example
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization
searchingindexing
x y+y3
x y+y3 x y y3 x y 3+
x y+y3 x y y3 x y 3+ id1id 2+id 2
3 id 1id 2 id 1
3
x y+y3 x y y3 x y 3+ id1id 2+id 2
3
id1id 2 id1
3 x y+ yconst yconst id 1id 2+id 2
const id 1const
x y+y3
x y+y2
x y+y2
x y+y 2 id 1id 2+id 2
2
x y+y 2 id1id 2+id 2
2
x y+yconst id 1id 2+id 2
const
x y+yconst id 1id 2+id 2
const Match
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formula processing example ndash subformulae weighting
(a+b2+c 0125)
(a+bc+2 0125)
(a 00875) (+ 00875) (bc+2 00875)
(b 006125) (c+2 006125)
(c 0042875)(+ 0042875)
(2 0042875)
(id 1+2 00343)
(c+const 0030625)
(id 1+const 001715)
(id 1id 2+2 007)
(bc+const 004375)
(id 1id 2+const 0035)
(id 1+id 2id 3+2 01)
(a+bc+const 00625)
(id 1+id 2id 3+const 005)
input
ordering
tokenization
variablesunification
constantsunification
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Implementation
bull Java
bull Lucene 310 now switching to LuceneSolr 4
bull Mathematical part implements Lucenersquos interface Tokenizer ndash able tointegrate to any Lucene based system
bull MIaS4Solr plugin was created for the use in Solr
bull Textual content ndash processed by StandardAnalyzer
bull easily deployable in JavaLucene based system or as a web service
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Search demonstration
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formulae search demonstration comments
Demo web interface httpaurafimunicz8085webmias
bull MathMLTEX input (Tralics [2] for conversion to MathML [])
bull Canonicalization of the query ndash problems with UMCL library [1]
bull Matched document snippet generation
bull MathJax for nicer math rendering and better portability
bull Snuggle TeX for on-the-fly as-you-type rendering
All up and ready on the EuDML system
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Searching (semantically) similar papers
Exploration of a DML browsing (semantically) similar papers
Semantic search via topic modeling Latent Semantic Indexing LatentDirichlet Allocation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Leading Edge Example Automated Meaning Picking from Texts
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Probabilistic Topical Modeling Latent Dirichlet Allocation
bull topic weighted list of words
bull document weighted list of topics
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Challenges of Math handling OCR indexing searchhellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Take care ldquoGod is in the detailsrdquo (Mies van der Rohe)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Data heterogenity specificity no free lunch to unify
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Document accessibility 4 DML processing challenges
Conversions (inversion of authoring+typesetting) needed from
born-digital period typesetting by TEX with export of [meta]data into digitallibrary maxTract
retro-digital period scanning geometrical transformations (BookRestorer)OCR (FineReader InftyReader) two-layer PDF
retro-born-digital period not complete tex or dvi data bad formats bitmapfonts of low resolution finally Tesseract
andDocument
Metaminusdata
RETROminusBORN DIGITAL
Image
Extraction
OCR
DataPrinted Analysis
Conversion
RETROminusDIGITISED
BORN DIGITAL
Refinement
Printed
PDFPS
TeX
Image CaptureDocument
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
From PDF to MathML (via LATEX)
Most fulltext available as PDF only often as low quality scanned volumepages Aggregation via IP protected OAI-PMH including the PDFs behingmoving wall
Workflow based in the case of
born-digital PDFs on maxTract otherwise on PDFBox (plain text)
bitmap PDFs on Infty otherwise on Tesseract (no math)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Infty from Fukuoka
Run in parallel in Brno Grenoble and Lisbon to speed up Almost 200Kpapers (more than 1M pages and still running)
Working with prof Suzuki to improve further (automation support forRussian LATEX driverhellip)
Automated only no time (and money) to fix OCR errors
MathML output used for [internal] indexing and similarity computations onlynot for metadata or export
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham II adding accessibility
Adding accessibility to mathematical documents on multiple levels
bull access to content for print impaired users such as those with visualimpairments dyslexia or dyspraxia
bull output compatible with web browsers screen readers and tools such ascopy and paste which is achieved by enriching the regular text withmathematical markup The output can also be used directly within thelimits of the presentation MathML produced as machine readablemathematical input to software systems such as Mathematica or Maple
On EuDML 10k+ fulltexts are served mostly for reading in Chrome (HTML5output) andor Adobe Acrobat Reader (as multiple-layer PDFs [no taggedPDFs yet])
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Metadata and conversions MathML and LATEX
Data heterogenity plethora of formats validation and conversions
world of authors LATEX TEX notation of mathematics
world of applicationsdata exchange XML MathML
REPOX engine (by IST Lisbon) to remap different metadata formats to uniquerepresentation
Metadata on the webmdashW3C standards MathML WAI-ARIA (WebAccessibility InitiativemdashAcessible Rich Internet Applications) WCAG (WebContent Accessibility Guidelines) 20
Big volumes rarr high automation to save costs converting to MathML (viaTralics) to allow discoverability and indexing (formulae similarity search)130+K fulltexts with MathML Infty still runninghellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Search
Vast amounts of [moving] contents in digital libraries from browsing tosearch from static links to indirect search links or even semantic search
Searching is crucial part of accessibility and exploration of the great ideasaround carved into 0s and 1s
Pragmatic decisions on math indexing level presentation vs content vssemantic In EuDML first step scalable presentation (structural) withmethods (tree indexing and weighting) extendable for content or semantic
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Math Search (MIR)
A picture is worth thousands words
ldquoA math formulae is worth of hundreds of wordsldquo (Ross Moore)
There are papers with more formulae than plain text
Precision vs Recall optimisation optimizing recall is better for exploratorysearching (we have not opted for precision as holy grail at the moment)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Motivation for MSE (including formulae) ndash cont
prof James Davenport CEIC member MKM2011 PC chair on panel atEuDML workshop in Bertinoro as a reply to the question ldquowhat functionalityand incentives would made a working mathematician to login and use amodern DML as EuDMLrdquo
ldquoMath formulae searchrdquo
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why math search is more relevant now than ever
bull Allowing formulas in queries helps to disambiguate and narrow searchSometimes the only difference among set of notionskey words would bein a math formula
bull Example 1 knowing the solution of partial differential equation in L1(C3)is there one in L2(C5)
bull Example 2 historians may want to follow the history of a (class of) formula(s)across languages and vocabularies (eg same objects studiedused byphysicists and mathematicians under different names)
bull Imagine your favourite ebook math textbook being [TEX]-search awaremdashegyour search app supports math formulae search
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
We did not start from scratch
Compare googlecomsearchq=Einstein with math-awaresearch of Einstein+$E=mcˆ2$ over arXiv
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Existing systems ndash pros and cons
bull MathDex formerly MathFind seven digit figure NSF grant by Design Science(Robert Miner) Lucene based indexing n-grams of presentation MathML pioneering conversion effort
bull EgoMath and EgoMath2 based on full text web search system Egothor presentation MathML for indexing idea of formulae augmentation α-equivalencealgorithms and relevance calculation
bull LATEXSearch MSE offered by Springer closed source only for LATEX math stringapproximate match based on strings no formulae structure matching smalldatabase 3 million formulae from lsquorandomrsquo sources
bull LeActiveMath indexing string tokens from OMDoc with OpenMath semanticnotation only for documents authored for LeActiveMath learning environment
bull DLMF only for documents authored for DLMF in special markup equation search
bull MathWeb Search semantic approach ndash uses substitution trees ndash not based on full
text searching supports Content MathML and OpenMath problem with acquiring
semantic data
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MIaS mdash Math Indexer and Searcher
bull math-aware full-text based search engine
bull joins textual and mathematical querying
bull MathML or TEX input
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MSE overall design
input document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
math
searching
indexing
Lucene Core
canonicalization
mat
h
Preprocessing into
canonicalized presentation
MathML
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Math indexing design
input canonicalized
document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
mat
h
math
searching
indexing
Lucene
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization canonicalization
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Example
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization
searchingindexing
x y+y3
x y+y3 x y y3 x y 3+
x y+y3 x y y3 x y 3+ id1id 2+id 2
3 id 1id 2 id 1
3
x y+y3 x y y3 x y 3+ id1id 2+id 2
3
id1id 2 id1
3 x y+ yconst yconst id 1id 2+id 2
const id 1const
x y+y3
x y+y2
x y+y2
x y+y 2 id 1id 2+id 2
2
x y+y 2 id1id 2+id 2
2
x y+yconst id 1id 2+id 2
const
x y+yconst id 1id 2+id 2
const Match
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formula processing example ndash subformulae weighting
(a+b2+c 0125)
(a+bc+2 0125)
(a 00875) (+ 00875) (bc+2 00875)
(b 006125) (c+2 006125)
(c 0042875)(+ 0042875)
(2 0042875)
(id 1+2 00343)
(c+const 0030625)
(id 1+const 001715)
(id 1id 2+2 007)
(bc+const 004375)
(id 1id 2+const 0035)
(id 1+id 2id 3+2 01)
(a+bc+const 00625)
(id 1+id 2id 3+const 005)
input
ordering
tokenization
variablesunification
constantsunification
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Implementation
bull Java
bull Lucene 310 now switching to LuceneSolr 4
bull Mathematical part implements Lucenersquos interface Tokenizer ndash able tointegrate to any Lucene based system
bull MIaS4Solr plugin was created for the use in Solr
bull Textual content ndash processed by StandardAnalyzer
bull easily deployable in JavaLucene based system or as a web service
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Search demonstration
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formulae search demonstration comments
Demo web interface httpaurafimunicz8085webmias
bull MathMLTEX input (Tralics [2] for conversion to MathML [])
bull Canonicalization of the query ndash problems with UMCL library [1]
bull Matched document snippet generation
bull MathJax for nicer math rendering and better portability
bull Snuggle TeX for on-the-fly as-you-type rendering
All up and ready on the EuDML system
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Searching (semantically) similar papers
Exploration of a DML browsing (semantically) similar papers
Semantic search via topic modeling Latent Semantic Indexing LatentDirichlet Allocation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Leading Edge Example Automated Meaning Picking from Texts
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Probabilistic Topical Modeling Latent Dirichlet Allocation
bull topic weighted list of words
bull document weighted list of topics
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Challenges of Math handling OCR indexing searchhellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Take care ldquoGod is in the detailsrdquo (Mies van der Rohe)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Data heterogenity specificity no free lunch to unify
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Document accessibility 4 DML processing challenges
Conversions (inversion of authoring+typesetting) needed from
born-digital period typesetting by TEX with export of [meta]data into digitallibrary maxTract
retro-digital period scanning geometrical transformations (BookRestorer)OCR (FineReader InftyReader) two-layer PDF
retro-born-digital period not complete tex or dvi data bad formats bitmapfonts of low resolution finally Tesseract
andDocument
Metaminusdata
RETROminusBORN DIGITAL
Image
Extraction
OCR
DataPrinted Analysis
Conversion
RETROminusDIGITISED
BORN DIGITAL
Refinement
Printed
PDFPS
TeX
Image CaptureDocument
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
From PDF to MathML (via LATEX)
Most fulltext available as PDF only often as low quality scanned volumepages Aggregation via IP protected OAI-PMH including the PDFs behingmoving wall
Workflow based in the case of
born-digital PDFs on maxTract otherwise on PDFBox (plain text)
bitmap PDFs on Infty otherwise on Tesseract (no math)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Infty from Fukuoka
Run in parallel in Brno Grenoble and Lisbon to speed up Almost 200Kpapers (more than 1M pages and still running)
Working with prof Suzuki to improve further (automation support forRussian LATEX driverhellip)
Automated only no time (and money) to fix OCR errors
MathML output used for [internal] indexing and similarity computations onlynot for metadata or export
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham II adding accessibility
Adding accessibility to mathematical documents on multiple levels
bull access to content for print impaired users such as those with visualimpairments dyslexia or dyspraxia
bull output compatible with web browsers screen readers and tools such ascopy and paste which is achieved by enriching the regular text withmathematical markup The output can also be used directly within thelimits of the presentation MathML produced as machine readablemathematical input to software systems such as Mathematica or Maple
On EuDML 10k+ fulltexts are served mostly for reading in Chrome (HTML5output) andor Adobe Acrobat Reader (as multiple-layer PDFs [no taggedPDFs yet])
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Metadata and conversions MathML and LATEX
Data heterogenity plethora of formats validation and conversions
world of authors LATEX TEX notation of mathematics
world of applicationsdata exchange XML MathML
REPOX engine (by IST Lisbon) to remap different metadata formats to uniquerepresentation
Metadata on the webmdashW3C standards MathML WAI-ARIA (WebAccessibility InitiativemdashAcessible Rich Internet Applications) WCAG (WebContent Accessibility Guidelines) 20
Big volumes rarr high automation to save costs converting to MathML (viaTralics) to allow discoverability and indexing (formulae similarity search)130+K fulltexts with MathML Infty still runninghellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Search
Vast amounts of [moving] contents in digital libraries from browsing tosearch from static links to indirect search links or even semantic search
Searching is crucial part of accessibility and exploration of the great ideasaround carved into 0s and 1s
Pragmatic decisions on math indexing level presentation vs content vssemantic In EuDML first step scalable presentation (structural) withmethods (tree indexing and weighting) extendable for content or semantic
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Math Search (MIR)
A picture is worth thousands words
ldquoA math formulae is worth of hundreds of wordsldquo (Ross Moore)
There are papers with more formulae than plain text
Precision vs Recall optimisation optimizing recall is better for exploratorysearching (we have not opted for precision as holy grail at the moment)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Motivation for MSE (including formulae) ndash cont
prof James Davenport CEIC member MKM2011 PC chair on panel atEuDML workshop in Bertinoro as a reply to the question ldquowhat functionalityand incentives would made a working mathematician to login and use amodern DML as EuDMLrdquo
ldquoMath formulae searchrdquo
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why math search is more relevant now than ever
bull Allowing formulas in queries helps to disambiguate and narrow searchSometimes the only difference among set of notionskey words would bein a math formula
bull Example 1 knowing the solution of partial differential equation in L1(C3)is there one in L2(C5)
bull Example 2 historians may want to follow the history of a (class of) formula(s)across languages and vocabularies (eg same objects studiedused byphysicists and mathematicians under different names)
bull Imagine your favourite ebook math textbook being [TEX]-search awaremdashegyour search app supports math formulae search
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
We did not start from scratch
Compare googlecomsearchq=Einstein with math-awaresearch of Einstein+$E=mcˆ2$ over arXiv
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Existing systems ndash pros and cons
bull MathDex formerly MathFind seven digit figure NSF grant by Design Science(Robert Miner) Lucene based indexing n-grams of presentation MathML pioneering conversion effort
bull EgoMath and EgoMath2 based on full text web search system Egothor presentation MathML for indexing idea of formulae augmentation α-equivalencealgorithms and relevance calculation
bull LATEXSearch MSE offered by Springer closed source only for LATEX math stringapproximate match based on strings no formulae structure matching smalldatabase 3 million formulae from lsquorandomrsquo sources
bull LeActiveMath indexing string tokens from OMDoc with OpenMath semanticnotation only for documents authored for LeActiveMath learning environment
bull DLMF only for documents authored for DLMF in special markup equation search
bull MathWeb Search semantic approach ndash uses substitution trees ndash not based on full
text searching supports Content MathML and OpenMath problem with acquiring
semantic data
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MIaS mdash Math Indexer and Searcher
bull math-aware full-text based search engine
bull joins textual and mathematical querying
bull MathML or TEX input
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MSE overall design
input document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
math
searching
indexing
Lucene Core
canonicalization
mat
h
Preprocessing into
canonicalized presentation
MathML
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Math indexing design
input canonicalized
document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
mat
h
math
searching
indexing
Lucene
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization canonicalization
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Example
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization
searchingindexing
x y+y3
x y+y3 x y y3 x y 3+
x y+y3 x y y3 x y 3+ id1id 2+id 2
3 id 1id 2 id 1
3
x y+y3 x y y3 x y 3+ id1id 2+id 2
3
id1id 2 id1
3 x y+ yconst yconst id 1id 2+id 2
const id 1const
x y+y3
x y+y2
x y+y2
x y+y 2 id 1id 2+id 2
2
x y+y 2 id1id 2+id 2
2
x y+yconst id 1id 2+id 2
const
x y+yconst id 1id 2+id 2
const Match
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formula processing example ndash subformulae weighting
(a+b2+c 0125)
(a+bc+2 0125)
(a 00875) (+ 00875) (bc+2 00875)
(b 006125) (c+2 006125)
(c 0042875)(+ 0042875)
(2 0042875)
(id 1+2 00343)
(c+const 0030625)
(id 1+const 001715)
(id 1id 2+2 007)
(bc+const 004375)
(id 1id 2+const 0035)
(id 1+id 2id 3+2 01)
(a+bc+const 00625)
(id 1+id 2id 3+const 005)
input
ordering
tokenization
variablesunification
constantsunification
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Implementation
bull Java
bull Lucene 310 now switching to LuceneSolr 4
bull Mathematical part implements Lucenersquos interface Tokenizer ndash able tointegrate to any Lucene based system
bull MIaS4Solr plugin was created for the use in Solr
bull Textual content ndash processed by StandardAnalyzer
bull easily deployable in JavaLucene based system or as a web service
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Search demonstration
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formulae search demonstration comments
Demo web interface httpaurafimunicz8085webmias
bull MathMLTEX input (Tralics [2] for conversion to MathML [])
bull Canonicalization of the query ndash problems with UMCL library [1]
bull Matched document snippet generation
bull MathJax for nicer math rendering and better portability
bull Snuggle TeX for on-the-fly as-you-type rendering
All up and ready on the EuDML system
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Searching (semantically) similar papers
Exploration of a DML browsing (semantically) similar papers
Semantic search via topic modeling Latent Semantic Indexing LatentDirichlet Allocation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Leading Edge Example Automated Meaning Picking from Texts
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Probabilistic Topical Modeling Latent Dirichlet Allocation
bull topic weighted list of words
bull document weighted list of topics
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Take care ldquoGod is in the detailsrdquo (Mies van der Rohe)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Data heterogenity specificity no free lunch to unify
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Document accessibility 4 DML processing challenges
Conversions (inversion of authoring+typesetting) needed from
born-digital period typesetting by TEX with export of [meta]data into digitallibrary maxTract
retro-digital period scanning geometrical transformations (BookRestorer)OCR (FineReader InftyReader) two-layer PDF
retro-born-digital period not complete tex or dvi data bad formats bitmapfonts of low resolution finally Tesseract
andDocument
Metaminusdata
RETROminusBORN DIGITAL
Image
Extraction
OCR
DataPrinted Analysis
Conversion
RETROminusDIGITISED
BORN DIGITAL
Refinement
Printed
PDFPS
TeX
Image CaptureDocument
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
From PDF to MathML (via LATEX)
Most fulltext available as PDF only often as low quality scanned volumepages Aggregation via IP protected OAI-PMH including the PDFs behingmoving wall
Workflow based in the case of
born-digital PDFs on maxTract otherwise on PDFBox (plain text)
bitmap PDFs on Infty otherwise on Tesseract (no math)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Infty from Fukuoka
Run in parallel in Brno Grenoble and Lisbon to speed up Almost 200Kpapers (more than 1M pages and still running)
Working with prof Suzuki to improve further (automation support forRussian LATEX driverhellip)
Automated only no time (and money) to fix OCR errors
MathML output used for [internal] indexing and similarity computations onlynot for metadata or export
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham II adding accessibility
Adding accessibility to mathematical documents on multiple levels
bull access to content for print impaired users such as those with visualimpairments dyslexia or dyspraxia
bull output compatible with web browsers screen readers and tools such ascopy and paste which is achieved by enriching the regular text withmathematical markup The output can also be used directly within thelimits of the presentation MathML produced as machine readablemathematical input to software systems such as Mathematica or Maple
On EuDML 10k+ fulltexts are served mostly for reading in Chrome (HTML5output) andor Adobe Acrobat Reader (as multiple-layer PDFs [no taggedPDFs yet])
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Metadata and conversions MathML and LATEX
Data heterogenity plethora of formats validation and conversions
world of authors LATEX TEX notation of mathematics
world of applicationsdata exchange XML MathML
REPOX engine (by IST Lisbon) to remap different metadata formats to uniquerepresentation
Metadata on the webmdashW3C standards MathML WAI-ARIA (WebAccessibility InitiativemdashAcessible Rich Internet Applications) WCAG (WebContent Accessibility Guidelines) 20
Big volumes rarr high automation to save costs converting to MathML (viaTralics) to allow discoverability and indexing (formulae similarity search)130+K fulltexts with MathML Infty still runninghellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Search
Vast amounts of [moving] contents in digital libraries from browsing tosearch from static links to indirect search links or even semantic search
Searching is crucial part of accessibility and exploration of the great ideasaround carved into 0s and 1s
Pragmatic decisions on math indexing level presentation vs content vssemantic In EuDML first step scalable presentation (structural) withmethods (tree indexing and weighting) extendable for content or semantic
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Math Search (MIR)
A picture is worth thousands words
ldquoA math formulae is worth of hundreds of wordsldquo (Ross Moore)
There are papers with more formulae than plain text
Precision vs Recall optimisation optimizing recall is better for exploratorysearching (we have not opted for precision as holy grail at the moment)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Motivation for MSE (including formulae) ndash cont
prof James Davenport CEIC member MKM2011 PC chair on panel atEuDML workshop in Bertinoro as a reply to the question ldquowhat functionalityand incentives would made a working mathematician to login and use amodern DML as EuDMLrdquo
ldquoMath formulae searchrdquo
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why math search is more relevant now than ever
bull Allowing formulas in queries helps to disambiguate and narrow searchSometimes the only difference among set of notionskey words would bein a math formula
bull Example 1 knowing the solution of partial differential equation in L1(C3)is there one in L2(C5)
bull Example 2 historians may want to follow the history of a (class of) formula(s)across languages and vocabularies (eg same objects studiedused byphysicists and mathematicians under different names)
bull Imagine your favourite ebook math textbook being [TEX]-search awaremdashegyour search app supports math formulae search
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
We did not start from scratch
Compare googlecomsearchq=Einstein with math-awaresearch of Einstein+$E=mcˆ2$ over arXiv
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Existing systems ndash pros and cons
bull MathDex formerly MathFind seven digit figure NSF grant by Design Science(Robert Miner) Lucene based indexing n-grams of presentation MathML pioneering conversion effort
bull EgoMath and EgoMath2 based on full text web search system Egothor presentation MathML for indexing idea of formulae augmentation α-equivalencealgorithms and relevance calculation
bull LATEXSearch MSE offered by Springer closed source only for LATEX math stringapproximate match based on strings no formulae structure matching smalldatabase 3 million formulae from lsquorandomrsquo sources
bull LeActiveMath indexing string tokens from OMDoc with OpenMath semanticnotation only for documents authored for LeActiveMath learning environment
bull DLMF only for documents authored for DLMF in special markup equation search
bull MathWeb Search semantic approach ndash uses substitution trees ndash not based on full
text searching supports Content MathML and OpenMath problem with acquiring
semantic data
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MIaS mdash Math Indexer and Searcher
bull math-aware full-text based search engine
bull joins textual and mathematical querying
bull MathML or TEX input
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MSE overall design
input document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
math
searching
indexing
Lucene Core
canonicalization
mat
h
Preprocessing into
canonicalized presentation
MathML
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Math indexing design
input canonicalized
document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
mat
h
math
searching
indexing
Lucene
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization canonicalization
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Example
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization
searchingindexing
x y+y3
x y+y3 x y y3 x y 3+
x y+y3 x y y3 x y 3+ id1id 2+id 2
3 id 1id 2 id 1
3
x y+y3 x y y3 x y 3+ id1id 2+id 2
3
id1id 2 id1
3 x y+ yconst yconst id 1id 2+id 2
const id 1const
x y+y3
x y+y2
x y+y2
x y+y 2 id 1id 2+id 2
2
x y+y 2 id1id 2+id 2
2
x y+yconst id 1id 2+id 2
const
x y+yconst id 1id 2+id 2
const Match
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formula processing example ndash subformulae weighting
(a+b2+c 0125)
(a+bc+2 0125)
(a 00875) (+ 00875) (bc+2 00875)
(b 006125) (c+2 006125)
(c 0042875)(+ 0042875)
(2 0042875)
(id 1+2 00343)
(c+const 0030625)
(id 1+const 001715)
(id 1id 2+2 007)
(bc+const 004375)
(id 1id 2+const 0035)
(id 1+id 2id 3+2 01)
(a+bc+const 00625)
(id 1+id 2id 3+const 005)
input
ordering
tokenization
variablesunification
constantsunification
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Implementation
bull Java
bull Lucene 310 now switching to LuceneSolr 4
bull Mathematical part implements Lucenersquos interface Tokenizer ndash able tointegrate to any Lucene based system
bull MIaS4Solr plugin was created for the use in Solr
bull Textual content ndash processed by StandardAnalyzer
bull easily deployable in JavaLucene based system or as a web service
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Search demonstration
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formulae search demonstration comments
Demo web interface httpaurafimunicz8085webmias
bull MathMLTEX input (Tralics [2] for conversion to MathML [])
bull Canonicalization of the query ndash problems with UMCL library [1]
bull Matched document snippet generation
bull MathJax for nicer math rendering and better portability
bull Snuggle TeX for on-the-fly as-you-type rendering
All up and ready on the EuDML system
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Searching (semantically) similar papers
Exploration of a DML browsing (semantically) similar papers
Semantic search via topic modeling Latent Semantic Indexing LatentDirichlet Allocation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Leading Edge Example Automated Meaning Picking from Texts
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Probabilistic Topical Modeling Latent Dirichlet Allocation
bull topic weighted list of words
bull document weighted list of topics
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Data heterogenity specificity no free lunch to unify
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Document accessibility 4 DML processing challenges
Conversions (inversion of authoring+typesetting) needed from
born-digital period typesetting by TEX with export of [meta]data into digitallibrary maxTract
retro-digital period scanning geometrical transformations (BookRestorer)OCR (FineReader InftyReader) two-layer PDF
retro-born-digital period not complete tex or dvi data bad formats bitmapfonts of low resolution finally Tesseract
andDocument
Metaminusdata
RETROminusBORN DIGITAL
Image
Extraction
OCR
DataPrinted Analysis
Conversion
RETROminusDIGITISED
BORN DIGITAL
Refinement
Printed
PDFPS
TeX
Image CaptureDocument
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
From PDF to MathML (via LATEX)
Most fulltext available as PDF only often as low quality scanned volumepages Aggregation via IP protected OAI-PMH including the PDFs behingmoving wall
Workflow based in the case of
born-digital PDFs on maxTract otherwise on PDFBox (plain text)
bitmap PDFs on Infty otherwise on Tesseract (no math)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Infty from Fukuoka
Run in parallel in Brno Grenoble and Lisbon to speed up Almost 200Kpapers (more than 1M pages and still running)
Working with prof Suzuki to improve further (automation support forRussian LATEX driverhellip)
Automated only no time (and money) to fix OCR errors
MathML output used for [internal] indexing and similarity computations onlynot for metadata or export
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham II adding accessibility
Adding accessibility to mathematical documents on multiple levels
bull access to content for print impaired users such as those with visualimpairments dyslexia or dyspraxia
bull output compatible with web browsers screen readers and tools such ascopy and paste which is achieved by enriching the regular text withmathematical markup The output can also be used directly within thelimits of the presentation MathML produced as machine readablemathematical input to software systems such as Mathematica or Maple
On EuDML 10k+ fulltexts are served mostly for reading in Chrome (HTML5output) andor Adobe Acrobat Reader (as multiple-layer PDFs [no taggedPDFs yet])
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Metadata and conversions MathML and LATEX
Data heterogenity plethora of formats validation and conversions
world of authors LATEX TEX notation of mathematics
world of applicationsdata exchange XML MathML
REPOX engine (by IST Lisbon) to remap different metadata formats to uniquerepresentation
Metadata on the webmdashW3C standards MathML WAI-ARIA (WebAccessibility InitiativemdashAcessible Rich Internet Applications) WCAG (WebContent Accessibility Guidelines) 20
Big volumes rarr high automation to save costs converting to MathML (viaTralics) to allow discoverability and indexing (formulae similarity search)130+K fulltexts with MathML Infty still runninghellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Search
Vast amounts of [moving] contents in digital libraries from browsing tosearch from static links to indirect search links or even semantic search
Searching is crucial part of accessibility and exploration of the great ideasaround carved into 0s and 1s
Pragmatic decisions on math indexing level presentation vs content vssemantic In EuDML first step scalable presentation (structural) withmethods (tree indexing and weighting) extendable for content or semantic
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Math Search (MIR)
A picture is worth thousands words
ldquoA math formulae is worth of hundreds of wordsldquo (Ross Moore)
There are papers with more formulae than plain text
Precision vs Recall optimisation optimizing recall is better for exploratorysearching (we have not opted for precision as holy grail at the moment)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Motivation for MSE (including formulae) ndash cont
prof James Davenport CEIC member MKM2011 PC chair on panel atEuDML workshop in Bertinoro as a reply to the question ldquowhat functionalityand incentives would made a working mathematician to login and use amodern DML as EuDMLrdquo
ldquoMath formulae searchrdquo
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why math search is more relevant now than ever
bull Allowing formulas in queries helps to disambiguate and narrow searchSometimes the only difference among set of notionskey words would bein a math formula
bull Example 1 knowing the solution of partial differential equation in L1(C3)is there one in L2(C5)
bull Example 2 historians may want to follow the history of a (class of) formula(s)across languages and vocabularies (eg same objects studiedused byphysicists and mathematicians under different names)
bull Imagine your favourite ebook math textbook being [TEX]-search awaremdashegyour search app supports math formulae search
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
We did not start from scratch
Compare googlecomsearchq=Einstein with math-awaresearch of Einstein+$E=mcˆ2$ over arXiv
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Existing systems ndash pros and cons
bull MathDex formerly MathFind seven digit figure NSF grant by Design Science(Robert Miner) Lucene based indexing n-grams of presentation MathML pioneering conversion effort
bull EgoMath and EgoMath2 based on full text web search system Egothor presentation MathML for indexing idea of formulae augmentation α-equivalencealgorithms and relevance calculation
bull LATEXSearch MSE offered by Springer closed source only for LATEX math stringapproximate match based on strings no formulae structure matching smalldatabase 3 million formulae from lsquorandomrsquo sources
bull LeActiveMath indexing string tokens from OMDoc with OpenMath semanticnotation only for documents authored for LeActiveMath learning environment
bull DLMF only for documents authored for DLMF in special markup equation search
bull MathWeb Search semantic approach ndash uses substitution trees ndash not based on full
text searching supports Content MathML and OpenMath problem with acquiring
semantic data
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MIaS mdash Math Indexer and Searcher
bull math-aware full-text based search engine
bull joins textual and mathematical querying
bull MathML or TEX input
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MSE overall design
input document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
math
searching
indexing
Lucene Core
canonicalization
mat
h
Preprocessing into
canonicalized presentation
MathML
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Math indexing design
input canonicalized
document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
mat
h
math
searching
indexing
Lucene
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization canonicalization
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Example
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization
searchingindexing
x y+y3
x y+y3 x y y3 x y 3+
x y+y3 x y y3 x y 3+ id1id 2+id 2
3 id 1id 2 id 1
3
x y+y3 x y y3 x y 3+ id1id 2+id 2
3
id1id 2 id1
3 x y+ yconst yconst id 1id 2+id 2
const id 1const
x y+y3
x y+y2
x y+y2
x y+y 2 id 1id 2+id 2
2
x y+y 2 id1id 2+id 2
2
x y+yconst id 1id 2+id 2
const
x y+yconst id 1id 2+id 2
const Match
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formula processing example ndash subformulae weighting
(a+b2+c 0125)
(a+bc+2 0125)
(a 00875) (+ 00875) (bc+2 00875)
(b 006125) (c+2 006125)
(c 0042875)(+ 0042875)
(2 0042875)
(id 1+2 00343)
(c+const 0030625)
(id 1+const 001715)
(id 1id 2+2 007)
(bc+const 004375)
(id 1id 2+const 0035)
(id 1+id 2id 3+2 01)
(a+bc+const 00625)
(id 1+id 2id 3+const 005)
input
ordering
tokenization
variablesunification
constantsunification
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Implementation
bull Java
bull Lucene 310 now switching to LuceneSolr 4
bull Mathematical part implements Lucenersquos interface Tokenizer ndash able tointegrate to any Lucene based system
bull MIaS4Solr plugin was created for the use in Solr
bull Textual content ndash processed by StandardAnalyzer
bull easily deployable in JavaLucene based system or as a web service
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Search demonstration
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formulae search demonstration comments
Demo web interface httpaurafimunicz8085webmias
bull MathMLTEX input (Tralics [2] for conversion to MathML [])
bull Canonicalization of the query ndash problems with UMCL library [1]
bull Matched document snippet generation
bull MathJax for nicer math rendering and better portability
bull Snuggle TeX for on-the-fly as-you-type rendering
All up and ready on the EuDML system
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Searching (semantically) similar papers
Exploration of a DML browsing (semantically) similar papers
Semantic search via topic modeling Latent Semantic Indexing LatentDirichlet Allocation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Leading Edge Example Automated Meaning Picking from Texts
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Probabilistic Topical Modeling Latent Dirichlet Allocation
bull topic weighted list of words
bull document weighted list of topics
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Document accessibility 4 DML processing challenges
Conversions (inversion of authoring+typesetting) needed from
born-digital period typesetting by TEX with export of [meta]data into digitallibrary maxTract
retro-digital period scanning geometrical transformations (BookRestorer)OCR (FineReader InftyReader) two-layer PDF
retro-born-digital period not complete tex or dvi data bad formats bitmapfonts of low resolution finally Tesseract
andDocument
Metaminusdata
RETROminusBORN DIGITAL
Image
Extraction
OCR
DataPrinted Analysis
Conversion
RETROminusDIGITISED
BORN DIGITAL
Refinement
Printed
PDFPS
TeX
Image CaptureDocument
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
From PDF to MathML (via LATEX)
Most fulltext available as PDF only often as low quality scanned volumepages Aggregation via IP protected OAI-PMH including the PDFs behingmoving wall
Workflow based in the case of
born-digital PDFs on maxTract otherwise on PDFBox (plain text)
bitmap PDFs on Infty otherwise on Tesseract (no math)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Infty from Fukuoka
Run in parallel in Brno Grenoble and Lisbon to speed up Almost 200Kpapers (more than 1M pages and still running)
Working with prof Suzuki to improve further (automation support forRussian LATEX driverhellip)
Automated only no time (and money) to fix OCR errors
MathML output used for [internal] indexing and similarity computations onlynot for metadata or export
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham II adding accessibility
Adding accessibility to mathematical documents on multiple levels
bull access to content for print impaired users such as those with visualimpairments dyslexia or dyspraxia
bull output compatible with web browsers screen readers and tools such ascopy and paste which is achieved by enriching the regular text withmathematical markup The output can also be used directly within thelimits of the presentation MathML produced as machine readablemathematical input to software systems such as Mathematica or Maple
On EuDML 10k+ fulltexts are served mostly for reading in Chrome (HTML5output) andor Adobe Acrobat Reader (as multiple-layer PDFs [no taggedPDFs yet])
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Metadata and conversions MathML and LATEX
Data heterogenity plethora of formats validation and conversions
world of authors LATEX TEX notation of mathematics
world of applicationsdata exchange XML MathML
REPOX engine (by IST Lisbon) to remap different metadata formats to uniquerepresentation
Metadata on the webmdashW3C standards MathML WAI-ARIA (WebAccessibility InitiativemdashAcessible Rich Internet Applications) WCAG (WebContent Accessibility Guidelines) 20
Big volumes rarr high automation to save costs converting to MathML (viaTralics) to allow discoverability and indexing (formulae similarity search)130+K fulltexts with MathML Infty still runninghellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Search
Vast amounts of [moving] contents in digital libraries from browsing tosearch from static links to indirect search links or even semantic search
Searching is crucial part of accessibility and exploration of the great ideasaround carved into 0s and 1s
Pragmatic decisions on math indexing level presentation vs content vssemantic In EuDML first step scalable presentation (structural) withmethods (tree indexing and weighting) extendable for content or semantic
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Math Search (MIR)
A picture is worth thousands words
ldquoA math formulae is worth of hundreds of wordsldquo (Ross Moore)
There are papers with more formulae than plain text
Precision vs Recall optimisation optimizing recall is better for exploratorysearching (we have not opted for precision as holy grail at the moment)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Motivation for MSE (including formulae) ndash cont
prof James Davenport CEIC member MKM2011 PC chair on panel atEuDML workshop in Bertinoro as a reply to the question ldquowhat functionalityand incentives would made a working mathematician to login and use amodern DML as EuDMLrdquo
ldquoMath formulae searchrdquo
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why math search is more relevant now than ever
bull Allowing formulas in queries helps to disambiguate and narrow searchSometimes the only difference among set of notionskey words would bein a math formula
bull Example 1 knowing the solution of partial differential equation in L1(C3)is there one in L2(C5)
bull Example 2 historians may want to follow the history of a (class of) formula(s)across languages and vocabularies (eg same objects studiedused byphysicists and mathematicians under different names)
bull Imagine your favourite ebook math textbook being [TEX]-search awaremdashegyour search app supports math formulae search
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
We did not start from scratch
Compare googlecomsearchq=Einstein with math-awaresearch of Einstein+$E=mcˆ2$ over arXiv
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Existing systems ndash pros and cons
bull MathDex formerly MathFind seven digit figure NSF grant by Design Science(Robert Miner) Lucene based indexing n-grams of presentation MathML pioneering conversion effort
bull EgoMath and EgoMath2 based on full text web search system Egothor presentation MathML for indexing idea of formulae augmentation α-equivalencealgorithms and relevance calculation
bull LATEXSearch MSE offered by Springer closed source only for LATEX math stringapproximate match based on strings no formulae structure matching smalldatabase 3 million formulae from lsquorandomrsquo sources
bull LeActiveMath indexing string tokens from OMDoc with OpenMath semanticnotation only for documents authored for LeActiveMath learning environment
bull DLMF only for documents authored for DLMF in special markup equation search
bull MathWeb Search semantic approach ndash uses substitution trees ndash not based on full
text searching supports Content MathML and OpenMath problem with acquiring
semantic data
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MIaS mdash Math Indexer and Searcher
bull math-aware full-text based search engine
bull joins textual and mathematical querying
bull MathML or TEX input
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MSE overall design
input document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
math
searching
indexing
Lucene Core
canonicalization
mat
h
Preprocessing into
canonicalized presentation
MathML
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Math indexing design
input canonicalized
document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
mat
h
math
searching
indexing
Lucene
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization canonicalization
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Example
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization
searchingindexing
x y+y3
x y+y3 x y y3 x y 3+
x y+y3 x y y3 x y 3+ id1id 2+id 2
3 id 1id 2 id 1
3
x y+y3 x y y3 x y 3+ id1id 2+id 2
3
id1id 2 id1
3 x y+ yconst yconst id 1id 2+id 2
const id 1const
x y+y3
x y+y2
x y+y2
x y+y 2 id 1id 2+id 2
2
x y+y 2 id1id 2+id 2
2
x y+yconst id 1id 2+id 2
const
x y+yconst id 1id 2+id 2
const Match
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formula processing example ndash subformulae weighting
(a+b2+c 0125)
(a+bc+2 0125)
(a 00875) (+ 00875) (bc+2 00875)
(b 006125) (c+2 006125)
(c 0042875)(+ 0042875)
(2 0042875)
(id 1+2 00343)
(c+const 0030625)
(id 1+const 001715)
(id 1id 2+2 007)
(bc+const 004375)
(id 1id 2+const 0035)
(id 1+id 2id 3+2 01)
(a+bc+const 00625)
(id 1+id 2id 3+const 005)
input
ordering
tokenization
variablesunification
constantsunification
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Implementation
bull Java
bull Lucene 310 now switching to LuceneSolr 4
bull Mathematical part implements Lucenersquos interface Tokenizer ndash able tointegrate to any Lucene based system
bull MIaS4Solr plugin was created for the use in Solr
bull Textual content ndash processed by StandardAnalyzer
bull easily deployable in JavaLucene based system or as a web service
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Search demonstration
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formulae search demonstration comments
Demo web interface httpaurafimunicz8085webmias
bull MathMLTEX input (Tralics [2] for conversion to MathML [])
bull Canonicalization of the query ndash problems with UMCL library [1]
bull Matched document snippet generation
bull MathJax for nicer math rendering and better portability
bull Snuggle TeX for on-the-fly as-you-type rendering
All up and ready on the EuDML system
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Searching (semantically) similar papers
Exploration of a DML browsing (semantically) similar papers
Semantic search via topic modeling Latent Semantic Indexing LatentDirichlet Allocation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Leading Edge Example Automated Meaning Picking from Texts
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Probabilistic Topical Modeling Latent Dirichlet Allocation
bull topic weighted list of words
bull document weighted list of topics
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
From PDF to MathML (via LATEX)
Most fulltext available as PDF only often as low quality scanned volumepages Aggregation via IP protected OAI-PMH including the PDFs behingmoving wall
Workflow based in the case of
born-digital PDFs on maxTract otherwise on PDFBox (plain text)
bitmap PDFs on Infty otherwise on Tesseract (no math)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Infty from Fukuoka
Run in parallel in Brno Grenoble and Lisbon to speed up Almost 200Kpapers (more than 1M pages and still running)
Working with prof Suzuki to improve further (automation support forRussian LATEX driverhellip)
Automated only no time (and money) to fix OCR errors
MathML output used for [internal] indexing and similarity computations onlynot for metadata or export
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham II adding accessibility
Adding accessibility to mathematical documents on multiple levels
bull access to content for print impaired users such as those with visualimpairments dyslexia or dyspraxia
bull output compatible with web browsers screen readers and tools such ascopy and paste which is achieved by enriching the regular text withmathematical markup The output can also be used directly within thelimits of the presentation MathML produced as machine readablemathematical input to software systems such as Mathematica or Maple
On EuDML 10k+ fulltexts are served mostly for reading in Chrome (HTML5output) andor Adobe Acrobat Reader (as multiple-layer PDFs [no taggedPDFs yet])
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Metadata and conversions MathML and LATEX
Data heterogenity plethora of formats validation and conversions
world of authors LATEX TEX notation of mathematics
world of applicationsdata exchange XML MathML
REPOX engine (by IST Lisbon) to remap different metadata formats to uniquerepresentation
Metadata on the webmdashW3C standards MathML WAI-ARIA (WebAccessibility InitiativemdashAcessible Rich Internet Applications) WCAG (WebContent Accessibility Guidelines) 20
Big volumes rarr high automation to save costs converting to MathML (viaTralics) to allow discoverability and indexing (formulae similarity search)130+K fulltexts with MathML Infty still runninghellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Search
Vast amounts of [moving] contents in digital libraries from browsing tosearch from static links to indirect search links or even semantic search
Searching is crucial part of accessibility and exploration of the great ideasaround carved into 0s and 1s
Pragmatic decisions on math indexing level presentation vs content vssemantic In EuDML first step scalable presentation (structural) withmethods (tree indexing and weighting) extendable for content or semantic
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Math Search (MIR)
A picture is worth thousands words
ldquoA math formulae is worth of hundreds of wordsldquo (Ross Moore)
There are papers with more formulae than plain text
Precision vs Recall optimisation optimizing recall is better for exploratorysearching (we have not opted for precision as holy grail at the moment)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Motivation for MSE (including formulae) ndash cont
prof James Davenport CEIC member MKM2011 PC chair on panel atEuDML workshop in Bertinoro as a reply to the question ldquowhat functionalityand incentives would made a working mathematician to login and use amodern DML as EuDMLrdquo
ldquoMath formulae searchrdquo
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why math search is more relevant now than ever
bull Allowing formulas in queries helps to disambiguate and narrow searchSometimes the only difference among set of notionskey words would bein a math formula
bull Example 1 knowing the solution of partial differential equation in L1(C3)is there one in L2(C5)
bull Example 2 historians may want to follow the history of a (class of) formula(s)across languages and vocabularies (eg same objects studiedused byphysicists and mathematicians under different names)
bull Imagine your favourite ebook math textbook being [TEX]-search awaremdashegyour search app supports math formulae search
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
We did not start from scratch
Compare googlecomsearchq=Einstein with math-awaresearch of Einstein+$E=mcˆ2$ over arXiv
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Existing systems ndash pros and cons
bull MathDex formerly MathFind seven digit figure NSF grant by Design Science(Robert Miner) Lucene based indexing n-grams of presentation MathML pioneering conversion effort
bull EgoMath and EgoMath2 based on full text web search system Egothor presentation MathML for indexing idea of formulae augmentation α-equivalencealgorithms and relevance calculation
bull LATEXSearch MSE offered by Springer closed source only for LATEX math stringapproximate match based on strings no formulae structure matching smalldatabase 3 million formulae from lsquorandomrsquo sources
bull LeActiveMath indexing string tokens from OMDoc with OpenMath semanticnotation only for documents authored for LeActiveMath learning environment
bull DLMF only for documents authored for DLMF in special markup equation search
bull MathWeb Search semantic approach ndash uses substitution trees ndash not based on full
text searching supports Content MathML and OpenMath problem with acquiring
semantic data
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MIaS mdash Math Indexer and Searcher
bull math-aware full-text based search engine
bull joins textual and mathematical querying
bull MathML or TEX input
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MSE overall design
input document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
math
searching
indexing
Lucene Core
canonicalization
mat
h
Preprocessing into
canonicalized presentation
MathML
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Math indexing design
input canonicalized
document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
mat
h
math
searching
indexing
Lucene
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization canonicalization
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Example
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization
searchingindexing
x y+y3
x y+y3 x y y3 x y 3+
x y+y3 x y y3 x y 3+ id1id 2+id 2
3 id 1id 2 id 1
3
x y+y3 x y y3 x y 3+ id1id 2+id 2
3
id1id 2 id1
3 x y+ yconst yconst id 1id 2+id 2
const id 1const
x y+y3
x y+y2
x y+y2
x y+y 2 id 1id 2+id 2
2
x y+y 2 id1id 2+id 2
2
x y+yconst id 1id 2+id 2
const
x y+yconst id 1id 2+id 2
const Match
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formula processing example ndash subformulae weighting
(a+b2+c 0125)
(a+bc+2 0125)
(a 00875) (+ 00875) (bc+2 00875)
(b 006125) (c+2 006125)
(c 0042875)(+ 0042875)
(2 0042875)
(id 1+2 00343)
(c+const 0030625)
(id 1+const 001715)
(id 1id 2+2 007)
(bc+const 004375)
(id 1id 2+const 0035)
(id 1+id 2id 3+2 01)
(a+bc+const 00625)
(id 1+id 2id 3+const 005)
input
ordering
tokenization
variablesunification
constantsunification
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Implementation
bull Java
bull Lucene 310 now switching to LuceneSolr 4
bull Mathematical part implements Lucenersquos interface Tokenizer ndash able tointegrate to any Lucene based system
bull MIaS4Solr plugin was created for the use in Solr
bull Textual content ndash processed by StandardAnalyzer
bull easily deployable in JavaLucene based system or as a web service
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Search demonstration
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formulae search demonstration comments
Demo web interface httpaurafimunicz8085webmias
bull MathMLTEX input (Tralics [2] for conversion to MathML [])
bull Canonicalization of the query ndash problems with UMCL library [1]
bull Matched document snippet generation
bull MathJax for nicer math rendering and better portability
bull Snuggle TeX for on-the-fly as-you-type rendering
All up and ready on the EuDML system
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Searching (semantically) similar papers
Exploration of a DML browsing (semantically) similar papers
Semantic search via topic modeling Latent Semantic Indexing LatentDirichlet Allocation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Leading Edge Example Automated Meaning Picking from Texts
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Probabilistic Topical Modeling Latent Dirichlet Allocation
bull topic weighted list of words
bull document weighted list of topics
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Infty from Fukuoka
Run in parallel in Brno Grenoble and Lisbon to speed up Almost 200Kpapers (more than 1M pages and still running)
Working with prof Suzuki to improve further (automation support forRussian LATEX driverhellip)
Automated only no time (and money) to fix OCR errors
MathML output used for [internal] indexing and similarity computations onlynot for metadata or export
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham II adding accessibility
Adding accessibility to mathematical documents on multiple levels
bull access to content for print impaired users such as those with visualimpairments dyslexia or dyspraxia
bull output compatible with web browsers screen readers and tools such ascopy and paste which is achieved by enriching the regular text withmathematical markup The output can also be used directly within thelimits of the presentation MathML produced as machine readablemathematical input to software systems such as Mathematica or Maple
On EuDML 10k+ fulltexts are served mostly for reading in Chrome (HTML5output) andor Adobe Acrobat Reader (as multiple-layer PDFs [no taggedPDFs yet])
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Metadata and conversions MathML and LATEX
Data heterogenity plethora of formats validation and conversions
world of authors LATEX TEX notation of mathematics
world of applicationsdata exchange XML MathML
REPOX engine (by IST Lisbon) to remap different metadata formats to uniquerepresentation
Metadata on the webmdashW3C standards MathML WAI-ARIA (WebAccessibility InitiativemdashAcessible Rich Internet Applications) WCAG (WebContent Accessibility Guidelines) 20
Big volumes rarr high automation to save costs converting to MathML (viaTralics) to allow discoverability and indexing (formulae similarity search)130+K fulltexts with MathML Infty still runninghellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Search
Vast amounts of [moving] contents in digital libraries from browsing tosearch from static links to indirect search links or even semantic search
Searching is crucial part of accessibility and exploration of the great ideasaround carved into 0s and 1s
Pragmatic decisions on math indexing level presentation vs content vssemantic In EuDML first step scalable presentation (structural) withmethods (tree indexing and weighting) extendable for content or semantic
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Math Search (MIR)
A picture is worth thousands words
ldquoA math formulae is worth of hundreds of wordsldquo (Ross Moore)
There are papers with more formulae than plain text
Precision vs Recall optimisation optimizing recall is better for exploratorysearching (we have not opted for precision as holy grail at the moment)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Motivation for MSE (including formulae) ndash cont
prof James Davenport CEIC member MKM2011 PC chair on panel atEuDML workshop in Bertinoro as a reply to the question ldquowhat functionalityand incentives would made a working mathematician to login and use amodern DML as EuDMLrdquo
ldquoMath formulae searchrdquo
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why math search is more relevant now than ever
bull Allowing formulas in queries helps to disambiguate and narrow searchSometimes the only difference among set of notionskey words would bein a math formula
bull Example 1 knowing the solution of partial differential equation in L1(C3)is there one in L2(C5)
bull Example 2 historians may want to follow the history of a (class of) formula(s)across languages and vocabularies (eg same objects studiedused byphysicists and mathematicians under different names)
bull Imagine your favourite ebook math textbook being [TEX]-search awaremdashegyour search app supports math formulae search
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
We did not start from scratch
Compare googlecomsearchq=Einstein with math-awaresearch of Einstein+$E=mcˆ2$ over arXiv
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Existing systems ndash pros and cons
bull MathDex formerly MathFind seven digit figure NSF grant by Design Science(Robert Miner) Lucene based indexing n-grams of presentation MathML pioneering conversion effort
bull EgoMath and EgoMath2 based on full text web search system Egothor presentation MathML for indexing idea of formulae augmentation α-equivalencealgorithms and relevance calculation
bull LATEXSearch MSE offered by Springer closed source only for LATEX math stringapproximate match based on strings no formulae structure matching smalldatabase 3 million formulae from lsquorandomrsquo sources
bull LeActiveMath indexing string tokens from OMDoc with OpenMath semanticnotation only for documents authored for LeActiveMath learning environment
bull DLMF only for documents authored for DLMF in special markup equation search
bull MathWeb Search semantic approach ndash uses substitution trees ndash not based on full
text searching supports Content MathML and OpenMath problem with acquiring
semantic data
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MIaS mdash Math Indexer and Searcher
bull math-aware full-text based search engine
bull joins textual and mathematical querying
bull MathML or TEX input
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MSE overall design
input document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
math
searching
indexing
Lucene Core
canonicalization
mat
h
Preprocessing into
canonicalized presentation
MathML
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Math indexing design
input canonicalized
document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
mat
h
math
searching
indexing
Lucene
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization canonicalization
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Example
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization
searchingindexing
x y+y3
x y+y3 x y y3 x y 3+
x y+y3 x y y3 x y 3+ id1id 2+id 2
3 id 1id 2 id 1
3
x y+y3 x y y3 x y 3+ id1id 2+id 2
3
id1id 2 id1
3 x y+ yconst yconst id 1id 2+id 2
const id 1const
x y+y3
x y+y2
x y+y2
x y+y 2 id 1id 2+id 2
2
x y+y 2 id1id 2+id 2
2
x y+yconst id 1id 2+id 2
const
x y+yconst id 1id 2+id 2
const Match
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formula processing example ndash subformulae weighting
(a+b2+c 0125)
(a+bc+2 0125)
(a 00875) (+ 00875) (bc+2 00875)
(b 006125) (c+2 006125)
(c 0042875)(+ 0042875)
(2 0042875)
(id 1+2 00343)
(c+const 0030625)
(id 1+const 001715)
(id 1id 2+2 007)
(bc+const 004375)
(id 1id 2+const 0035)
(id 1+id 2id 3+2 01)
(a+bc+const 00625)
(id 1+id 2id 3+const 005)
input
ordering
tokenization
variablesunification
constantsunification
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Implementation
bull Java
bull Lucene 310 now switching to LuceneSolr 4
bull Mathematical part implements Lucenersquos interface Tokenizer ndash able tointegrate to any Lucene based system
bull MIaS4Solr plugin was created for the use in Solr
bull Textual content ndash processed by StandardAnalyzer
bull easily deployable in JavaLucene based system or as a web service
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Search demonstration
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formulae search demonstration comments
Demo web interface httpaurafimunicz8085webmias
bull MathMLTEX input (Tralics [2] for conversion to MathML [])
bull Canonicalization of the query ndash problems with UMCL library [1]
bull Matched document snippet generation
bull MathJax for nicer math rendering and better portability
bull Snuggle TeX for on-the-fly as-you-type rendering
All up and ready on the EuDML system
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Searching (semantically) similar papers
Exploration of a DML browsing (semantically) similar papers
Semantic search via topic modeling Latent Semantic Indexing LatentDirichlet Allocation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Leading Edge Example Automated Meaning Picking from Texts
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Probabilistic Topical Modeling Latent Dirichlet Allocation
bull topic weighted list of words
bull document weighted list of topics
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham II adding accessibility
Adding accessibility to mathematical documents on multiple levels
bull access to content for print impaired users such as those with visualimpairments dyslexia or dyspraxia
bull output compatible with web browsers screen readers and tools such ascopy and paste which is achieved by enriching the regular text withmathematical markup The output can also be used directly within thelimits of the presentation MathML produced as machine readablemathematical input to software systems such as Mathematica or Maple
On EuDML 10k+ fulltexts are served mostly for reading in Chrome (HTML5output) andor Adobe Acrobat Reader (as multiple-layer PDFs [no taggedPDFs yet])
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Metadata and conversions MathML and LATEX
Data heterogenity plethora of formats validation and conversions
world of authors LATEX TEX notation of mathematics
world of applicationsdata exchange XML MathML
REPOX engine (by IST Lisbon) to remap different metadata formats to uniquerepresentation
Metadata on the webmdashW3C standards MathML WAI-ARIA (WebAccessibility InitiativemdashAcessible Rich Internet Applications) WCAG (WebContent Accessibility Guidelines) 20
Big volumes rarr high automation to save costs converting to MathML (viaTralics) to allow discoverability and indexing (formulae similarity search)130+K fulltexts with MathML Infty still runninghellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Search
Vast amounts of [moving] contents in digital libraries from browsing tosearch from static links to indirect search links or even semantic search
Searching is crucial part of accessibility and exploration of the great ideasaround carved into 0s and 1s
Pragmatic decisions on math indexing level presentation vs content vssemantic In EuDML first step scalable presentation (structural) withmethods (tree indexing and weighting) extendable for content or semantic
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Math Search (MIR)
A picture is worth thousands words
ldquoA math formulae is worth of hundreds of wordsldquo (Ross Moore)
There are papers with more formulae than plain text
Precision vs Recall optimisation optimizing recall is better for exploratorysearching (we have not opted for precision as holy grail at the moment)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Motivation for MSE (including formulae) ndash cont
prof James Davenport CEIC member MKM2011 PC chair on panel atEuDML workshop in Bertinoro as a reply to the question ldquowhat functionalityand incentives would made a working mathematician to login and use amodern DML as EuDMLrdquo
ldquoMath formulae searchrdquo
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why math search is more relevant now than ever
bull Allowing formulas in queries helps to disambiguate and narrow searchSometimes the only difference among set of notionskey words would bein a math formula
bull Example 1 knowing the solution of partial differential equation in L1(C3)is there one in L2(C5)
bull Example 2 historians may want to follow the history of a (class of) formula(s)across languages and vocabularies (eg same objects studiedused byphysicists and mathematicians under different names)
bull Imagine your favourite ebook math textbook being [TEX]-search awaremdashegyour search app supports math formulae search
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
We did not start from scratch
Compare googlecomsearchq=Einstein with math-awaresearch of Einstein+$E=mcˆ2$ over arXiv
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Existing systems ndash pros and cons
bull MathDex formerly MathFind seven digit figure NSF grant by Design Science(Robert Miner) Lucene based indexing n-grams of presentation MathML pioneering conversion effort
bull EgoMath and EgoMath2 based on full text web search system Egothor presentation MathML for indexing idea of formulae augmentation α-equivalencealgorithms and relevance calculation
bull LATEXSearch MSE offered by Springer closed source only for LATEX math stringapproximate match based on strings no formulae structure matching smalldatabase 3 million formulae from lsquorandomrsquo sources
bull LeActiveMath indexing string tokens from OMDoc with OpenMath semanticnotation only for documents authored for LeActiveMath learning environment
bull DLMF only for documents authored for DLMF in special markup equation search
bull MathWeb Search semantic approach ndash uses substitution trees ndash not based on full
text searching supports Content MathML and OpenMath problem with acquiring
semantic data
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MIaS mdash Math Indexer and Searcher
bull math-aware full-text based search engine
bull joins textual and mathematical querying
bull MathML or TEX input
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MSE overall design
input document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
math
searching
indexing
Lucene Core
canonicalization
mat
h
Preprocessing into
canonicalized presentation
MathML
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Math indexing design
input canonicalized
document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
mat
h
math
searching
indexing
Lucene
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization canonicalization
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Example
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization
searchingindexing
x y+y3
x y+y3 x y y3 x y 3+
x y+y3 x y y3 x y 3+ id1id 2+id 2
3 id 1id 2 id 1
3
x y+y3 x y y3 x y 3+ id1id 2+id 2
3
id1id 2 id1
3 x y+ yconst yconst id 1id 2+id 2
const id 1const
x y+y3
x y+y2
x y+y2
x y+y 2 id 1id 2+id 2
2
x y+y 2 id1id 2+id 2
2
x y+yconst id 1id 2+id 2
const
x y+yconst id 1id 2+id 2
const Match
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formula processing example ndash subformulae weighting
(a+b2+c 0125)
(a+bc+2 0125)
(a 00875) (+ 00875) (bc+2 00875)
(b 006125) (c+2 006125)
(c 0042875)(+ 0042875)
(2 0042875)
(id 1+2 00343)
(c+const 0030625)
(id 1+const 001715)
(id 1id 2+2 007)
(bc+const 004375)
(id 1id 2+const 0035)
(id 1+id 2id 3+2 01)
(a+bc+const 00625)
(id 1+id 2id 3+const 005)
input
ordering
tokenization
variablesunification
constantsunification
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Implementation
bull Java
bull Lucene 310 now switching to LuceneSolr 4
bull Mathematical part implements Lucenersquos interface Tokenizer ndash able tointegrate to any Lucene based system
bull MIaS4Solr plugin was created for the use in Solr
bull Textual content ndash processed by StandardAnalyzer
bull easily deployable in JavaLucene based system or as a web service
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Search demonstration
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formulae search demonstration comments
Demo web interface httpaurafimunicz8085webmias
bull MathMLTEX input (Tralics [2] for conversion to MathML [])
bull Canonicalization of the query ndash problems with UMCL library [1]
bull Matched document snippet generation
bull MathJax for nicer math rendering and better portability
bull Snuggle TeX for on-the-fly as-you-type rendering
All up and ready on the EuDML system
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Searching (semantically) similar papers
Exploration of a DML browsing (semantically) similar papers
Semantic search via topic modeling Latent Semantic Indexing LatentDirichlet Allocation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Leading Edge Example Automated Meaning Picking from Texts
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Probabilistic Topical Modeling Latent Dirichlet Allocation
bull topic weighted list of words
bull document weighted list of topics
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
maxTract from Birmingham II adding accessibility
Adding accessibility to mathematical documents on multiple levels
bull access to content for print impaired users such as those with visualimpairments dyslexia or dyspraxia
bull output compatible with web browsers screen readers and tools such ascopy and paste which is achieved by enriching the regular text withmathematical markup The output can also be used directly within thelimits of the presentation MathML produced as machine readablemathematical input to software systems such as Mathematica or Maple
On EuDML 10k+ fulltexts are served mostly for reading in Chrome (HTML5output) andor Adobe Acrobat Reader (as multiple-layer PDFs [no taggedPDFs yet])
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Metadata and conversions MathML and LATEX
Data heterogenity plethora of formats validation and conversions
world of authors LATEX TEX notation of mathematics
world of applicationsdata exchange XML MathML
REPOX engine (by IST Lisbon) to remap different metadata formats to uniquerepresentation
Metadata on the webmdashW3C standards MathML WAI-ARIA (WebAccessibility InitiativemdashAcessible Rich Internet Applications) WCAG (WebContent Accessibility Guidelines) 20
Big volumes rarr high automation to save costs converting to MathML (viaTralics) to allow discoverability and indexing (formulae similarity search)130+K fulltexts with MathML Infty still runninghellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Search
Vast amounts of [moving] contents in digital libraries from browsing tosearch from static links to indirect search links or even semantic search
Searching is crucial part of accessibility and exploration of the great ideasaround carved into 0s and 1s
Pragmatic decisions on math indexing level presentation vs content vssemantic In EuDML first step scalable presentation (structural) withmethods (tree indexing and weighting) extendable for content or semantic
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Math Search (MIR)
A picture is worth thousands words
ldquoA math formulae is worth of hundreds of wordsldquo (Ross Moore)
There are papers with more formulae than plain text
Precision vs Recall optimisation optimizing recall is better for exploratorysearching (we have not opted for precision as holy grail at the moment)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Motivation for MSE (including formulae) ndash cont
prof James Davenport CEIC member MKM2011 PC chair on panel atEuDML workshop in Bertinoro as a reply to the question ldquowhat functionalityand incentives would made a working mathematician to login and use amodern DML as EuDMLrdquo
ldquoMath formulae searchrdquo
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why math search is more relevant now than ever
bull Allowing formulas in queries helps to disambiguate and narrow searchSometimes the only difference among set of notionskey words would bein a math formula
bull Example 1 knowing the solution of partial differential equation in L1(C3)is there one in L2(C5)
bull Example 2 historians may want to follow the history of a (class of) formula(s)across languages and vocabularies (eg same objects studiedused byphysicists and mathematicians under different names)
bull Imagine your favourite ebook math textbook being [TEX]-search awaremdashegyour search app supports math formulae search
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
We did not start from scratch
Compare googlecomsearchq=Einstein with math-awaresearch of Einstein+$E=mcˆ2$ over arXiv
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Existing systems ndash pros and cons
bull MathDex formerly MathFind seven digit figure NSF grant by Design Science(Robert Miner) Lucene based indexing n-grams of presentation MathML pioneering conversion effort
bull EgoMath and EgoMath2 based on full text web search system Egothor presentation MathML for indexing idea of formulae augmentation α-equivalencealgorithms and relevance calculation
bull LATEXSearch MSE offered by Springer closed source only for LATEX math stringapproximate match based on strings no formulae structure matching smalldatabase 3 million formulae from lsquorandomrsquo sources
bull LeActiveMath indexing string tokens from OMDoc with OpenMath semanticnotation only for documents authored for LeActiveMath learning environment
bull DLMF only for documents authored for DLMF in special markup equation search
bull MathWeb Search semantic approach ndash uses substitution trees ndash not based on full
text searching supports Content MathML and OpenMath problem with acquiring
semantic data
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MIaS mdash Math Indexer and Searcher
bull math-aware full-text based search engine
bull joins textual and mathematical querying
bull MathML or TEX input
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MSE overall design
input document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
math
searching
indexing
Lucene Core
canonicalization
mat
h
Preprocessing into
canonicalized presentation
MathML
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Math indexing design
input canonicalized
document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
mat
h
math
searching
indexing
Lucene
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization canonicalization
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Example
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization
searchingindexing
x y+y3
x y+y3 x y y3 x y 3+
x y+y3 x y y3 x y 3+ id1id 2+id 2
3 id 1id 2 id 1
3
x y+y3 x y y3 x y 3+ id1id 2+id 2
3
id1id 2 id1
3 x y+ yconst yconst id 1id 2+id 2
const id 1const
x y+y3
x y+y2
x y+y2
x y+y 2 id 1id 2+id 2
2
x y+y 2 id1id 2+id 2
2
x y+yconst id 1id 2+id 2
const
x y+yconst id 1id 2+id 2
const Match
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formula processing example ndash subformulae weighting
(a+b2+c 0125)
(a+bc+2 0125)
(a 00875) (+ 00875) (bc+2 00875)
(b 006125) (c+2 006125)
(c 0042875)(+ 0042875)
(2 0042875)
(id 1+2 00343)
(c+const 0030625)
(id 1+const 001715)
(id 1id 2+2 007)
(bc+const 004375)
(id 1id 2+const 0035)
(id 1+id 2id 3+2 01)
(a+bc+const 00625)
(id 1+id 2id 3+const 005)
input
ordering
tokenization
variablesunification
constantsunification
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Implementation
bull Java
bull Lucene 310 now switching to LuceneSolr 4
bull Mathematical part implements Lucenersquos interface Tokenizer ndash able tointegrate to any Lucene based system
bull MIaS4Solr plugin was created for the use in Solr
bull Textual content ndash processed by StandardAnalyzer
bull easily deployable in JavaLucene based system or as a web service
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Search demonstration
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formulae search demonstration comments
Demo web interface httpaurafimunicz8085webmias
bull MathMLTEX input (Tralics [2] for conversion to MathML [])
bull Canonicalization of the query ndash problems with UMCL library [1]
bull Matched document snippet generation
bull MathJax for nicer math rendering and better portability
bull Snuggle TeX for on-the-fly as-you-type rendering
All up and ready on the EuDML system
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Searching (semantically) similar papers
Exploration of a DML browsing (semantically) similar papers
Semantic search via topic modeling Latent Semantic Indexing LatentDirichlet Allocation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Leading Edge Example Automated Meaning Picking from Texts
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Probabilistic Topical Modeling Latent Dirichlet Allocation
bull topic weighted list of words
bull document weighted list of topics
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Metadata and conversions MathML and LATEX
Data heterogenity plethora of formats validation and conversions
world of authors LATEX TEX notation of mathematics
world of applicationsdata exchange XML MathML
REPOX engine (by IST Lisbon) to remap different metadata formats to uniquerepresentation
Metadata on the webmdashW3C standards MathML WAI-ARIA (WebAccessibility InitiativemdashAcessible Rich Internet Applications) WCAG (WebContent Accessibility Guidelines) 20
Big volumes rarr high automation to save costs converting to MathML (viaTralics) to allow discoverability and indexing (formulae similarity search)130+K fulltexts with MathML Infty still runninghellip
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Search
Vast amounts of [moving] contents in digital libraries from browsing tosearch from static links to indirect search links or even semantic search
Searching is crucial part of accessibility and exploration of the great ideasaround carved into 0s and 1s
Pragmatic decisions on math indexing level presentation vs content vssemantic In EuDML first step scalable presentation (structural) withmethods (tree indexing and weighting) extendable for content or semantic
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Math Search (MIR)
A picture is worth thousands words
ldquoA math formulae is worth of hundreds of wordsldquo (Ross Moore)
There are papers with more formulae than plain text
Precision vs Recall optimisation optimizing recall is better for exploratorysearching (we have not opted for precision as holy grail at the moment)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Motivation for MSE (including formulae) ndash cont
prof James Davenport CEIC member MKM2011 PC chair on panel atEuDML workshop in Bertinoro as a reply to the question ldquowhat functionalityand incentives would made a working mathematician to login and use amodern DML as EuDMLrdquo
ldquoMath formulae searchrdquo
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why math search is more relevant now than ever
bull Allowing formulas in queries helps to disambiguate and narrow searchSometimes the only difference among set of notionskey words would bein a math formula
bull Example 1 knowing the solution of partial differential equation in L1(C3)is there one in L2(C5)
bull Example 2 historians may want to follow the history of a (class of) formula(s)across languages and vocabularies (eg same objects studiedused byphysicists and mathematicians under different names)
bull Imagine your favourite ebook math textbook being [TEX]-search awaremdashegyour search app supports math formulae search
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
We did not start from scratch
Compare googlecomsearchq=Einstein with math-awaresearch of Einstein+$E=mcˆ2$ over arXiv
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Existing systems ndash pros and cons
bull MathDex formerly MathFind seven digit figure NSF grant by Design Science(Robert Miner) Lucene based indexing n-grams of presentation MathML pioneering conversion effort
bull EgoMath and EgoMath2 based on full text web search system Egothor presentation MathML for indexing idea of formulae augmentation α-equivalencealgorithms and relevance calculation
bull LATEXSearch MSE offered by Springer closed source only for LATEX math stringapproximate match based on strings no formulae structure matching smalldatabase 3 million formulae from lsquorandomrsquo sources
bull LeActiveMath indexing string tokens from OMDoc with OpenMath semanticnotation only for documents authored for LeActiveMath learning environment
bull DLMF only for documents authored for DLMF in special markup equation search
bull MathWeb Search semantic approach ndash uses substitution trees ndash not based on full
text searching supports Content MathML and OpenMath problem with acquiring
semantic data
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MIaS mdash Math Indexer and Searcher
bull math-aware full-text based search engine
bull joins textual and mathematical querying
bull MathML or TEX input
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MSE overall design
input document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
math
searching
indexing
Lucene Core
canonicalization
mat
h
Preprocessing into
canonicalized presentation
MathML
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Math indexing design
input canonicalized
document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
mat
h
math
searching
indexing
Lucene
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization canonicalization
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Example
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization
searchingindexing
x y+y3
x y+y3 x y y3 x y 3+
x y+y3 x y y3 x y 3+ id1id 2+id 2
3 id 1id 2 id 1
3
x y+y3 x y y3 x y 3+ id1id 2+id 2
3
id1id 2 id1
3 x y+ yconst yconst id 1id 2+id 2
const id 1const
x y+y3
x y+y2
x y+y2
x y+y 2 id 1id 2+id 2
2
x y+y 2 id1id 2+id 2
2
x y+yconst id 1id 2+id 2
const
x y+yconst id 1id 2+id 2
const Match
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formula processing example ndash subformulae weighting
(a+b2+c 0125)
(a+bc+2 0125)
(a 00875) (+ 00875) (bc+2 00875)
(b 006125) (c+2 006125)
(c 0042875)(+ 0042875)
(2 0042875)
(id 1+2 00343)
(c+const 0030625)
(id 1+const 001715)
(id 1id 2+2 007)
(bc+const 004375)
(id 1id 2+const 0035)
(id 1+id 2id 3+2 01)
(a+bc+const 00625)
(id 1+id 2id 3+const 005)
input
ordering
tokenization
variablesunification
constantsunification
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Implementation
bull Java
bull Lucene 310 now switching to LuceneSolr 4
bull Mathematical part implements Lucenersquos interface Tokenizer ndash able tointegrate to any Lucene based system
bull MIaS4Solr plugin was created for the use in Solr
bull Textual content ndash processed by StandardAnalyzer
bull easily deployable in JavaLucene based system or as a web service
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Search demonstration
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formulae search demonstration comments
Demo web interface httpaurafimunicz8085webmias
bull MathMLTEX input (Tralics [2] for conversion to MathML [])
bull Canonicalization of the query ndash problems with UMCL library [1]
bull Matched document snippet generation
bull MathJax for nicer math rendering and better portability
bull Snuggle TeX for on-the-fly as-you-type rendering
All up and ready on the EuDML system
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Searching (semantically) similar papers
Exploration of a DML browsing (semantically) similar papers
Semantic search via topic modeling Latent Semantic Indexing LatentDirichlet Allocation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Leading Edge Example Automated Meaning Picking from Texts
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Probabilistic Topical Modeling Latent Dirichlet Allocation
bull topic weighted list of words
bull document weighted list of topics
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Search
Vast amounts of [moving] contents in digital libraries from browsing tosearch from static links to indirect search links or even semantic search
Searching is crucial part of accessibility and exploration of the great ideasaround carved into 0s and 1s
Pragmatic decisions on math indexing level presentation vs content vssemantic In EuDML first step scalable presentation (structural) withmethods (tree indexing and weighting) extendable for content or semantic
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Math Search (MIR)
A picture is worth thousands words
ldquoA math formulae is worth of hundreds of wordsldquo (Ross Moore)
There are papers with more formulae than plain text
Precision vs Recall optimisation optimizing recall is better for exploratorysearching (we have not opted for precision as holy grail at the moment)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Motivation for MSE (including formulae) ndash cont
prof James Davenport CEIC member MKM2011 PC chair on panel atEuDML workshop in Bertinoro as a reply to the question ldquowhat functionalityand incentives would made a working mathematician to login and use amodern DML as EuDMLrdquo
ldquoMath formulae searchrdquo
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why math search is more relevant now than ever
bull Allowing formulas in queries helps to disambiguate and narrow searchSometimes the only difference among set of notionskey words would bein a math formula
bull Example 1 knowing the solution of partial differential equation in L1(C3)is there one in L2(C5)
bull Example 2 historians may want to follow the history of a (class of) formula(s)across languages and vocabularies (eg same objects studiedused byphysicists and mathematicians under different names)
bull Imagine your favourite ebook math textbook being [TEX]-search awaremdashegyour search app supports math formulae search
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
We did not start from scratch
Compare googlecomsearchq=Einstein with math-awaresearch of Einstein+$E=mcˆ2$ over arXiv
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Existing systems ndash pros and cons
bull MathDex formerly MathFind seven digit figure NSF grant by Design Science(Robert Miner) Lucene based indexing n-grams of presentation MathML pioneering conversion effort
bull EgoMath and EgoMath2 based on full text web search system Egothor presentation MathML for indexing idea of formulae augmentation α-equivalencealgorithms and relevance calculation
bull LATEXSearch MSE offered by Springer closed source only for LATEX math stringapproximate match based on strings no formulae structure matching smalldatabase 3 million formulae from lsquorandomrsquo sources
bull LeActiveMath indexing string tokens from OMDoc with OpenMath semanticnotation only for documents authored for LeActiveMath learning environment
bull DLMF only for documents authored for DLMF in special markup equation search
bull MathWeb Search semantic approach ndash uses substitution trees ndash not based on full
text searching supports Content MathML and OpenMath problem with acquiring
semantic data
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MIaS mdash Math Indexer and Searcher
bull math-aware full-text based search engine
bull joins textual and mathematical querying
bull MathML or TEX input
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MSE overall design
input document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
math
searching
indexing
Lucene Core
canonicalization
mat
h
Preprocessing into
canonicalized presentation
MathML
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Math indexing design
input canonicalized
document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
mat
h
math
searching
indexing
Lucene
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization canonicalization
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Example
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization
searchingindexing
x y+y3
x y+y3 x y y3 x y 3+
x y+y3 x y y3 x y 3+ id1id 2+id 2
3 id 1id 2 id 1
3
x y+y3 x y y3 x y 3+ id1id 2+id 2
3
id1id 2 id1
3 x y+ yconst yconst id 1id 2+id 2
const id 1const
x y+y3
x y+y2
x y+y2
x y+y 2 id 1id 2+id 2
2
x y+y 2 id1id 2+id 2
2
x y+yconst id 1id 2+id 2
const
x y+yconst id 1id 2+id 2
const Match
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formula processing example ndash subformulae weighting
(a+b2+c 0125)
(a+bc+2 0125)
(a 00875) (+ 00875) (bc+2 00875)
(b 006125) (c+2 006125)
(c 0042875)(+ 0042875)
(2 0042875)
(id 1+2 00343)
(c+const 0030625)
(id 1+const 001715)
(id 1id 2+2 007)
(bc+const 004375)
(id 1id 2+const 0035)
(id 1+id 2id 3+2 01)
(a+bc+const 00625)
(id 1+id 2id 3+const 005)
input
ordering
tokenization
variablesunification
constantsunification
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Implementation
bull Java
bull Lucene 310 now switching to LuceneSolr 4
bull Mathematical part implements Lucenersquos interface Tokenizer ndash able tointegrate to any Lucene based system
bull MIaS4Solr plugin was created for the use in Solr
bull Textual content ndash processed by StandardAnalyzer
bull easily deployable in JavaLucene based system or as a web service
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Search demonstration
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formulae search demonstration comments
Demo web interface httpaurafimunicz8085webmias
bull MathMLTEX input (Tralics [2] for conversion to MathML [])
bull Canonicalization of the query ndash problems with UMCL library [1]
bull Matched document snippet generation
bull MathJax for nicer math rendering and better portability
bull Snuggle TeX for on-the-fly as-you-type rendering
All up and ready on the EuDML system
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Searching (semantically) similar papers
Exploration of a DML browsing (semantically) similar papers
Semantic search via topic modeling Latent Semantic Indexing LatentDirichlet Allocation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Leading Edge Example Automated Meaning Picking from Texts
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Probabilistic Topical Modeling Latent Dirichlet Allocation
bull topic weighted list of words
bull document weighted list of topics
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why Math Search (MIR)
A picture is worth thousands words
ldquoA math formulae is worth of hundreds of wordsldquo (Ross Moore)
There are papers with more formulae than plain text
Precision vs Recall optimisation optimizing recall is better for exploratorysearching (we have not opted for precision as holy grail at the moment)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Motivation for MSE (including formulae) ndash cont
prof James Davenport CEIC member MKM2011 PC chair on panel atEuDML workshop in Bertinoro as a reply to the question ldquowhat functionalityand incentives would made a working mathematician to login and use amodern DML as EuDMLrdquo
ldquoMath formulae searchrdquo
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why math search is more relevant now than ever
bull Allowing formulas in queries helps to disambiguate and narrow searchSometimes the only difference among set of notionskey words would bein a math formula
bull Example 1 knowing the solution of partial differential equation in L1(C3)is there one in L2(C5)
bull Example 2 historians may want to follow the history of a (class of) formula(s)across languages and vocabularies (eg same objects studiedused byphysicists and mathematicians under different names)
bull Imagine your favourite ebook math textbook being [TEX]-search awaremdashegyour search app supports math formulae search
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
We did not start from scratch
Compare googlecomsearchq=Einstein with math-awaresearch of Einstein+$E=mcˆ2$ over arXiv
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Existing systems ndash pros and cons
bull MathDex formerly MathFind seven digit figure NSF grant by Design Science(Robert Miner) Lucene based indexing n-grams of presentation MathML pioneering conversion effort
bull EgoMath and EgoMath2 based on full text web search system Egothor presentation MathML for indexing idea of formulae augmentation α-equivalencealgorithms and relevance calculation
bull LATEXSearch MSE offered by Springer closed source only for LATEX math stringapproximate match based on strings no formulae structure matching smalldatabase 3 million formulae from lsquorandomrsquo sources
bull LeActiveMath indexing string tokens from OMDoc with OpenMath semanticnotation only for documents authored for LeActiveMath learning environment
bull DLMF only for documents authored for DLMF in special markup equation search
bull MathWeb Search semantic approach ndash uses substitution trees ndash not based on full
text searching supports Content MathML and OpenMath problem with acquiring
semantic data
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MIaS mdash Math Indexer and Searcher
bull math-aware full-text based search engine
bull joins textual and mathematical querying
bull MathML or TEX input
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MSE overall design
input document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
math
searching
indexing
Lucene Core
canonicalization
mat
h
Preprocessing into
canonicalized presentation
MathML
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Math indexing design
input canonicalized
document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
mat
h
math
searching
indexing
Lucene
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization canonicalization
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Example
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization
searchingindexing
x y+y3
x y+y3 x y y3 x y 3+
x y+y3 x y y3 x y 3+ id1id 2+id 2
3 id 1id 2 id 1
3
x y+y3 x y y3 x y 3+ id1id 2+id 2
3
id1id 2 id1
3 x y+ yconst yconst id 1id 2+id 2
const id 1const
x y+y3
x y+y2
x y+y2
x y+y 2 id 1id 2+id 2
2
x y+y 2 id1id 2+id 2
2
x y+yconst id 1id 2+id 2
const
x y+yconst id 1id 2+id 2
const Match
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formula processing example ndash subformulae weighting
(a+b2+c 0125)
(a+bc+2 0125)
(a 00875) (+ 00875) (bc+2 00875)
(b 006125) (c+2 006125)
(c 0042875)(+ 0042875)
(2 0042875)
(id 1+2 00343)
(c+const 0030625)
(id 1+const 001715)
(id 1id 2+2 007)
(bc+const 004375)
(id 1id 2+const 0035)
(id 1+id 2id 3+2 01)
(a+bc+const 00625)
(id 1+id 2id 3+const 005)
input
ordering
tokenization
variablesunification
constantsunification
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Implementation
bull Java
bull Lucene 310 now switching to LuceneSolr 4
bull Mathematical part implements Lucenersquos interface Tokenizer ndash able tointegrate to any Lucene based system
bull MIaS4Solr plugin was created for the use in Solr
bull Textual content ndash processed by StandardAnalyzer
bull easily deployable in JavaLucene based system or as a web service
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Search demonstration
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formulae search demonstration comments
Demo web interface httpaurafimunicz8085webmias
bull MathMLTEX input (Tralics [2] for conversion to MathML [])
bull Canonicalization of the query ndash problems with UMCL library [1]
bull Matched document snippet generation
bull MathJax for nicer math rendering and better portability
bull Snuggle TeX for on-the-fly as-you-type rendering
All up and ready on the EuDML system
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Searching (semantically) similar papers
Exploration of a DML browsing (semantically) similar papers
Semantic search via topic modeling Latent Semantic Indexing LatentDirichlet Allocation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Leading Edge Example Automated Meaning Picking from Texts
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Probabilistic Topical Modeling Latent Dirichlet Allocation
bull topic weighted list of words
bull document weighted list of topics
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Motivation for MSE (including formulae) ndash cont
prof James Davenport CEIC member MKM2011 PC chair on panel atEuDML workshop in Bertinoro as a reply to the question ldquowhat functionalityand incentives would made a working mathematician to login and use amodern DML as EuDMLrdquo
ldquoMath formulae searchrdquo
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why math search is more relevant now than ever
bull Allowing formulas in queries helps to disambiguate and narrow searchSometimes the only difference among set of notionskey words would bein a math formula
bull Example 1 knowing the solution of partial differential equation in L1(C3)is there one in L2(C5)
bull Example 2 historians may want to follow the history of a (class of) formula(s)across languages and vocabularies (eg same objects studiedused byphysicists and mathematicians under different names)
bull Imagine your favourite ebook math textbook being [TEX]-search awaremdashegyour search app supports math formulae search
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
We did not start from scratch
Compare googlecomsearchq=Einstein with math-awaresearch of Einstein+$E=mcˆ2$ over arXiv
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Existing systems ndash pros and cons
bull MathDex formerly MathFind seven digit figure NSF grant by Design Science(Robert Miner) Lucene based indexing n-grams of presentation MathML pioneering conversion effort
bull EgoMath and EgoMath2 based on full text web search system Egothor presentation MathML for indexing idea of formulae augmentation α-equivalencealgorithms and relevance calculation
bull LATEXSearch MSE offered by Springer closed source only for LATEX math stringapproximate match based on strings no formulae structure matching smalldatabase 3 million formulae from lsquorandomrsquo sources
bull LeActiveMath indexing string tokens from OMDoc with OpenMath semanticnotation only for documents authored for LeActiveMath learning environment
bull DLMF only for documents authored for DLMF in special markup equation search
bull MathWeb Search semantic approach ndash uses substitution trees ndash not based on full
text searching supports Content MathML and OpenMath problem with acquiring
semantic data
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MIaS mdash Math Indexer and Searcher
bull math-aware full-text based search engine
bull joins textual and mathematical querying
bull MathML or TEX input
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MSE overall design
input document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
math
searching
indexing
Lucene Core
canonicalization
mat
h
Preprocessing into
canonicalized presentation
MathML
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Math indexing design
input canonicalized
document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
mat
h
math
searching
indexing
Lucene
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization canonicalization
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Example
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization
searchingindexing
x y+y3
x y+y3 x y y3 x y 3+
x y+y3 x y y3 x y 3+ id1id 2+id 2
3 id 1id 2 id 1
3
x y+y3 x y y3 x y 3+ id1id 2+id 2
3
id1id 2 id1
3 x y+ yconst yconst id 1id 2+id 2
const id 1const
x y+y3
x y+y2
x y+y2
x y+y 2 id 1id 2+id 2
2
x y+y 2 id1id 2+id 2
2
x y+yconst id 1id 2+id 2
const
x y+yconst id 1id 2+id 2
const Match
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formula processing example ndash subformulae weighting
(a+b2+c 0125)
(a+bc+2 0125)
(a 00875) (+ 00875) (bc+2 00875)
(b 006125) (c+2 006125)
(c 0042875)(+ 0042875)
(2 0042875)
(id 1+2 00343)
(c+const 0030625)
(id 1+const 001715)
(id 1id 2+2 007)
(bc+const 004375)
(id 1id 2+const 0035)
(id 1+id 2id 3+2 01)
(a+bc+const 00625)
(id 1+id 2id 3+const 005)
input
ordering
tokenization
variablesunification
constantsunification
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Implementation
bull Java
bull Lucene 310 now switching to LuceneSolr 4
bull Mathematical part implements Lucenersquos interface Tokenizer ndash able tointegrate to any Lucene based system
bull MIaS4Solr plugin was created for the use in Solr
bull Textual content ndash processed by StandardAnalyzer
bull easily deployable in JavaLucene based system or as a web service
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Search demonstration
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formulae search demonstration comments
Demo web interface httpaurafimunicz8085webmias
bull MathMLTEX input (Tralics [2] for conversion to MathML [])
bull Canonicalization of the query ndash problems with UMCL library [1]
bull Matched document snippet generation
bull MathJax for nicer math rendering and better portability
bull Snuggle TeX for on-the-fly as-you-type rendering
All up and ready on the EuDML system
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Searching (semantically) similar papers
Exploration of a DML browsing (semantically) similar papers
Semantic search via topic modeling Latent Semantic Indexing LatentDirichlet Allocation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Leading Edge Example Automated Meaning Picking from Texts
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Probabilistic Topical Modeling Latent Dirichlet Allocation
bull topic weighted list of words
bull document weighted list of topics
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Why math search is more relevant now than ever
bull Allowing formulas in queries helps to disambiguate and narrow searchSometimes the only difference among set of notionskey words would bein a math formula
bull Example 1 knowing the solution of partial differential equation in L1(C3)is there one in L2(C5)
bull Example 2 historians may want to follow the history of a (class of) formula(s)across languages and vocabularies (eg same objects studiedused byphysicists and mathematicians under different names)
bull Imagine your favourite ebook math textbook being [TEX]-search awaremdashegyour search app supports math formulae search
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
We did not start from scratch
Compare googlecomsearchq=Einstein with math-awaresearch of Einstein+$E=mcˆ2$ over arXiv
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Existing systems ndash pros and cons
bull MathDex formerly MathFind seven digit figure NSF grant by Design Science(Robert Miner) Lucene based indexing n-grams of presentation MathML pioneering conversion effort
bull EgoMath and EgoMath2 based on full text web search system Egothor presentation MathML for indexing idea of formulae augmentation α-equivalencealgorithms and relevance calculation
bull LATEXSearch MSE offered by Springer closed source only for LATEX math stringapproximate match based on strings no formulae structure matching smalldatabase 3 million formulae from lsquorandomrsquo sources
bull LeActiveMath indexing string tokens from OMDoc with OpenMath semanticnotation only for documents authored for LeActiveMath learning environment
bull DLMF only for documents authored for DLMF in special markup equation search
bull MathWeb Search semantic approach ndash uses substitution trees ndash not based on full
text searching supports Content MathML and OpenMath problem with acquiring
semantic data
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MIaS mdash Math Indexer and Searcher
bull math-aware full-text based search engine
bull joins textual and mathematical querying
bull MathML or TEX input
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MSE overall design
input document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
math
searching
indexing
Lucene Core
canonicalization
mat
h
Preprocessing into
canonicalized presentation
MathML
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Math indexing design
input canonicalized
document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
mat
h
math
searching
indexing
Lucene
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization canonicalization
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Example
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization
searchingindexing
x y+y3
x y+y3 x y y3 x y 3+
x y+y3 x y y3 x y 3+ id1id 2+id 2
3 id 1id 2 id 1
3
x y+y3 x y y3 x y 3+ id1id 2+id 2
3
id1id 2 id1
3 x y+ yconst yconst id 1id 2+id 2
const id 1const
x y+y3
x y+y2
x y+y2
x y+y 2 id 1id 2+id 2
2
x y+y 2 id1id 2+id 2
2
x y+yconst id 1id 2+id 2
const
x y+yconst id 1id 2+id 2
const Match
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formula processing example ndash subformulae weighting
(a+b2+c 0125)
(a+bc+2 0125)
(a 00875) (+ 00875) (bc+2 00875)
(b 006125) (c+2 006125)
(c 0042875)(+ 0042875)
(2 0042875)
(id 1+2 00343)
(c+const 0030625)
(id 1+const 001715)
(id 1id 2+2 007)
(bc+const 004375)
(id 1id 2+const 0035)
(id 1+id 2id 3+2 01)
(a+bc+const 00625)
(id 1+id 2id 3+const 005)
input
ordering
tokenization
variablesunification
constantsunification
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Implementation
bull Java
bull Lucene 310 now switching to LuceneSolr 4
bull Mathematical part implements Lucenersquos interface Tokenizer ndash able tointegrate to any Lucene based system
bull MIaS4Solr plugin was created for the use in Solr
bull Textual content ndash processed by StandardAnalyzer
bull easily deployable in JavaLucene based system or as a web service
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Search demonstration
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formulae search demonstration comments
Demo web interface httpaurafimunicz8085webmias
bull MathMLTEX input (Tralics [2] for conversion to MathML [])
bull Canonicalization of the query ndash problems with UMCL library [1]
bull Matched document snippet generation
bull MathJax for nicer math rendering and better portability
bull Snuggle TeX for on-the-fly as-you-type rendering
All up and ready on the EuDML system
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Searching (semantically) similar papers
Exploration of a DML browsing (semantically) similar papers
Semantic search via topic modeling Latent Semantic Indexing LatentDirichlet Allocation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Leading Edge Example Automated Meaning Picking from Texts
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Probabilistic Topical Modeling Latent Dirichlet Allocation
bull topic weighted list of words
bull document weighted list of topics
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
We did not start from scratch
Compare googlecomsearchq=Einstein with math-awaresearch of Einstein+$E=mcˆ2$ over arXiv
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Existing systems ndash pros and cons
bull MathDex formerly MathFind seven digit figure NSF grant by Design Science(Robert Miner) Lucene based indexing n-grams of presentation MathML pioneering conversion effort
bull EgoMath and EgoMath2 based on full text web search system Egothor presentation MathML for indexing idea of formulae augmentation α-equivalencealgorithms and relevance calculation
bull LATEXSearch MSE offered by Springer closed source only for LATEX math stringapproximate match based on strings no formulae structure matching smalldatabase 3 million formulae from lsquorandomrsquo sources
bull LeActiveMath indexing string tokens from OMDoc with OpenMath semanticnotation only for documents authored for LeActiveMath learning environment
bull DLMF only for documents authored for DLMF in special markup equation search
bull MathWeb Search semantic approach ndash uses substitution trees ndash not based on full
text searching supports Content MathML and OpenMath problem with acquiring
semantic data
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MIaS mdash Math Indexer and Searcher
bull math-aware full-text based search engine
bull joins textual and mathematical querying
bull MathML or TEX input
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MSE overall design
input document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
math
searching
indexing
Lucene Core
canonicalization
mat
h
Preprocessing into
canonicalized presentation
MathML
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Math indexing design
input canonicalized
document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
mat
h
math
searching
indexing
Lucene
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization canonicalization
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Example
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization
searchingindexing
x y+y3
x y+y3 x y y3 x y 3+
x y+y3 x y y3 x y 3+ id1id 2+id 2
3 id 1id 2 id 1
3
x y+y3 x y y3 x y 3+ id1id 2+id 2
3
id1id 2 id1
3 x y+ yconst yconst id 1id 2+id 2
const id 1const
x y+y3
x y+y2
x y+y2
x y+y 2 id 1id 2+id 2
2
x y+y 2 id1id 2+id 2
2
x y+yconst id 1id 2+id 2
const
x y+yconst id 1id 2+id 2
const Match
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formula processing example ndash subformulae weighting
(a+b2+c 0125)
(a+bc+2 0125)
(a 00875) (+ 00875) (bc+2 00875)
(b 006125) (c+2 006125)
(c 0042875)(+ 0042875)
(2 0042875)
(id 1+2 00343)
(c+const 0030625)
(id 1+const 001715)
(id 1id 2+2 007)
(bc+const 004375)
(id 1id 2+const 0035)
(id 1+id 2id 3+2 01)
(a+bc+const 00625)
(id 1+id 2id 3+const 005)
input
ordering
tokenization
variablesunification
constantsunification
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Implementation
bull Java
bull Lucene 310 now switching to LuceneSolr 4
bull Mathematical part implements Lucenersquos interface Tokenizer ndash able tointegrate to any Lucene based system
bull MIaS4Solr plugin was created for the use in Solr
bull Textual content ndash processed by StandardAnalyzer
bull easily deployable in JavaLucene based system or as a web service
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Search demonstration
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formulae search demonstration comments
Demo web interface httpaurafimunicz8085webmias
bull MathMLTEX input (Tralics [2] for conversion to MathML [])
bull Canonicalization of the query ndash problems with UMCL library [1]
bull Matched document snippet generation
bull MathJax for nicer math rendering and better portability
bull Snuggle TeX for on-the-fly as-you-type rendering
All up and ready on the EuDML system
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Searching (semantically) similar papers
Exploration of a DML browsing (semantically) similar papers
Semantic search via topic modeling Latent Semantic Indexing LatentDirichlet Allocation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Leading Edge Example Automated Meaning Picking from Texts
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Probabilistic Topical Modeling Latent Dirichlet Allocation
bull topic weighted list of words
bull document weighted list of topics
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Existing systems ndash pros and cons
bull MathDex formerly MathFind seven digit figure NSF grant by Design Science(Robert Miner) Lucene based indexing n-grams of presentation MathML pioneering conversion effort
bull EgoMath and EgoMath2 based on full text web search system Egothor presentation MathML for indexing idea of formulae augmentation α-equivalencealgorithms and relevance calculation
bull LATEXSearch MSE offered by Springer closed source only for LATEX math stringapproximate match based on strings no formulae structure matching smalldatabase 3 million formulae from lsquorandomrsquo sources
bull LeActiveMath indexing string tokens from OMDoc with OpenMath semanticnotation only for documents authored for LeActiveMath learning environment
bull DLMF only for documents authored for DLMF in special markup equation search
bull MathWeb Search semantic approach ndash uses substitution trees ndash not based on full
text searching supports Content MathML and OpenMath problem with acquiring
semantic data
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MIaS mdash Math Indexer and Searcher
bull math-aware full-text based search engine
bull joins textual and mathematical querying
bull MathML or TEX input
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MSE overall design
input document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
math
searching
indexing
Lucene Core
canonicalization
mat
h
Preprocessing into
canonicalized presentation
MathML
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Math indexing design
input canonicalized
document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
mat
h
math
searching
indexing
Lucene
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization canonicalization
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Example
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization
searchingindexing
x y+y3
x y+y3 x y y3 x y 3+
x y+y3 x y y3 x y 3+ id1id 2+id 2
3 id 1id 2 id 1
3
x y+y3 x y y3 x y 3+ id1id 2+id 2
3
id1id 2 id1
3 x y+ yconst yconst id 1id 2+id 2
const id 1const
x y+y3
x y+y2
x y+y2
x y+y 2 id 1id 2+id 2
2
x y+y 2 id1id 2+id 2
2
x y+yconst id 1id 2+id 2
const
x y+yconst id 1id 2+id 2
const Match
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formula processing example ndash subformulae weighting
(a+b2+c 0125)
(a+bc+2 0125)
(a 00875) (+ 00875) (bc+2 00875)
(b 006125) (c+2 006125)
(c 0042875)(+ 0042875)
(2 0042875)
(id 1+2 00343)
(c+const 0030625)
(id 1+const 001715)
(id 1id 2+2 007)
(bc+const 004375)
(id 1id 2+const 0035)
(id 1+id 2id 3+2 01)
(a+bc+const 00625)
(id 1+id 2id 3+const 005)
input
ordering
tokenization
variablesunification
constantsunification
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Implementation
bull Java
bull Lucene 310 now switching to LuceneSolr 4
bull Mathematical part implements Lucenersquos interface Tokenizer ndash able tointegrate to any Lucene based system
bull MIaS4Solr plugin was created for the use in Solr
bull Textual content ndash processed by StandardAnalyzer
bull easily deployable in JavaLucene based system or as a web service
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Search demonstration
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formulae search demonstration comments
Demo web interface httpaurafimunicz8085webmias
bull MathMLTEX input (Tralics [2] for conversion to MathML [])
bull Canonicalization of the query ndash problems with UMCL library [1]
bull Matched document snippet generation
bull MathJax for nicer math rendering and better portability
bull Snuggle TeX for on-the-fly as-you-type rendering
All up and ready on the EuDML system
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Searching (semantically) similar papers
Exploration of a DML browsing (semantically) similar papers
Semantic search via topic modeling Latent Semantic Indexing LatentDirichlet Allocation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Leading Edge Example Automated Meaning Picking from Texts
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Probabilistic Topical Modeling Latent Dirichlet Allocation
bull topic weighted list of words
bull document weighted list of topics
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MIaS mdash Math Indexer and Searcher
bull math-aware full-text based search engine
bull joins textual and mathematical querying
bull MathML or TEX input
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MSE overall design
input document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
math
searching
indexing
Lucene Core
canonicalization
mat
h
Preprocessing into
canonicalized presentation
MathML
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Math indexing design
input canonicalized
document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
mat
h
math
searching
indexing
Lucene
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization canonicalization
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Example
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization
searchingindexing
x y+y3
x y+y3 x y y3 x y 3+
x y+y3 x y y3 x y 3+ id1id 2+id 2
3 id 1id 2 id 1
3
x y+y3 x y y3 x y 3+ id1id 2+id 2
3
id1id 2 id1
3 x y+ yconst yconst id 1id 2+id 2
const id 1const
x y+y3
x y+y2
x y+y2
x y+y 2 id 1id 2+id 2
2
x y+y 2 id1id 2+id 2
2
x y+yconst id 1id 2+id 2
const
x y+yconst id 1id 2+id 2
const Match
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formula processing example ndash subformulae weighting
(a+b2+c 0125)
(a+bc+2 0125)
(a 00875) (+ 00875) (bc+2 00875)
(b 006125) (c+2 006125)
(c 0042875)(+ 0042875)
(2 0042875)
(id 1+2 00343)
(c+const 0030625)
(id 1+const 001715)
(id 1id 2+2 007)
(bc+const 004375)
(id 1id 2+const 0035)
(id 1+id 2id 3+2 01)
(a+bc+const 00625)
(id 1+id 2id 3+const 005)
input
ordering
tokenization
variablesunification
constantsunification
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Implementation
bull Java
bull Lucene 310 now switching to LuceneSolr 4
bull Mathematical part implements Lucenersquos interface Tokenizer ndash able tointegrate to any Lucene based system
bull MIaS4Solr plugin was created for the use in Solr
bull Textual content ndash processed by StandardAnalyzer
bull easily deployable in JavaLucene based system or as a web service
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Search demonstration
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formulae search demonstration comments
Demo web interface httpaurafimunicz8085webmias
bull MathMLTEX input (Tralics [2] for conversion to MathML [])
bull Canonicalization of the query ndash problems with UMCL library [1]
bull Matched document snippet generation
bull MathJax for nicer math rendering and better portability
bull Snuggle TeX for on-the-fly as-you-type rendering
All up and ready on the EuDML system
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Searching (semantically) similar papers
Exploration of a DML browsing (semantically) similar papers
Semantic search via topic modeling Latent Semantic Indexing LatentDirichlet Allocation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Leading Edge Example Automated Meaning Picking from Texts
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Probabilistic Topical Modeling Latent Dirichlet Allocation
bull topic weighted list of words
bull document weighted list of topics
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
MSE overall design
input document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
math
searching
indexing
Lucene Core
canonicalization
mat
h
Preprocessing into
canonicalized presentation
MathML
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Math indexing design
input canonicalized
document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
mat
h
math
searching
indexing
Lucene
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization canonicalization
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Example
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization
searchingindexing
x y+y3
x y+y3 x y y3 x y 3+
x y+y3 x y y3 x y 3+ id1id 2+id 2
3 id 1id 2 id 1
3
x y+y3 x y y3 x y 3+ id1id 2+id 2
3
id1id 2 id1
3 x y+ yconst yconst id 1id 2+id 2
const id 1const
x y+y3
x y+y2
x y+y2
x y+y 2 id 1id 2+id 2
2
x y+y 2 id1id 2+id 2
2
x y+yconst id 1id 2+id 2
const
x y+yconst id 1id 2+id 2
const Match
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formula processing example ndash subformulae weighting
(a+b2+c 0125)
(a+bc+2 0125)
(a 00875) (+ 00875) (bc+2 00875)
(b 006125) (c+2 006125)
(c 0042875)(+ 0042875)
(2 0042875)
(id 1+2 00343)
(c+const 0030625)
(id 1+const 001715)
(id 1id 2+2 007)
(bc+const 004375)
(id 1id 2+const 0035)
(id 1+id 2id 3+2 01)
(a+bc+const 00625)
(id 1+id 2id 3+const 005)
input
ordering
tokenization
variablesunification
constantsunification
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Implementation
bull Java
bull Lucene 310 now switching to LuceneSolr 4
bull Mathematical part implements Lucenersquos interface Tokenizer ndash able tointegrate to any Lucene based system
bull MIaS4Solr plugin was created for the use in Solr
bull Textual content ndash processed by StandardAnalyzer
bull easily deployable in JavaLucene based system or as a web service
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Search demonstration
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formulae search demonstration comments
Demo web interface httpaurafimunicz8085webmias
bull MathMLTEX input (Tralics [2] for conversion to MathML [])
bull Canonicalization of the query ndash problems with UMCL library [1]
bull Matched document snippet generation
bull MathJax for nicer math rendering and better portability
bull Snuggle TeX for on-the-fly as-you-type rendering
All up and ready on the EuDML system
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Searching (semantically) similar papers
Exploration of a DML browsing (semantically) similar papers
Semantic search via topic modeling Latent Semantic Indexing LatentDirichlet Allocation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Leading Edge Example Automated Meaning Picking from Texts
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Probabilistic Topical Modeling Latent Dirichlet Allocation
bull topic weighted list of words
bull document weighted list of topics
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Math indexing design
input canonicalized
document
document handler
text
searcher
input query
text
terms
queryresults
index
indexer
unification
math processing
tokenization
mat
h
math
searching
indexing
Lucene
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization canonicalization
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Example
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization
searchingindexing
x y+y3
x y+y3 x y y3 x y 3+
x y+y3 x y y3 x y 3+ id1id 2+id 2
3 id 1id 2 id 1
3
x y+y3 x y y3 x y 3+ id1id 2+id 2
3
id1id 2 id1
3 x y+ yconst yconst id 1id 2+id 2
const id 1const
x y+y3
x y+y2
x y+y2
x y+y 2 id 1id 2+id 2
2
x y+y 2 id1id 2+id 2
2
x y+yconst id 1id 2+id 2
const
x y+yconst id 1id 2+id 2
const Match
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formula processing example ndash subformulae weighting
(a+b2+c 0125)
(a+bc+2 0125)
(a 00875) (+ 00875) (bc+2 00875)
(b 006125) (c+2 006125)
(c 0042875)(+ 0042875)
(2 0042875)
(id 1+2 00343)
(c+const 0030625)
(id 1+const 001715)
(id 1id 2+2 007)
(bc+const 004375)
(id 1id 2+const 0035)
(id 1+id 2id 3+2 01)
(a+bc+const 00625)
(id 1+id 2id 3+const 005)
input
ordering
tokenization
variablesunification
constantsunification
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Implementation
bull Java
bull Lucene 310 now switching to LuceneSolr 4
bull Mathematical part implements Lucenersquos interface Tokenizer ndash able tointegrate to any Lucene based system
bull MIaS4Solr plugin was created for the use in Solr
bull Textual content ndash processed by StandardAnalyzer
bull easily deployable in JavaLucene based system or as a web service
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Search demonstration
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formulae search demonstration comments
Demo web interface httpaurafimunicz8085webmias
bull MathMLTEX input (Tralics [2] for conversion to MathML [])
bull Canonicalization of the query ndash problems with UMCL library [1]
bull Matched document snippet generation
bull MathJax for nicer math rendering and better portability
bull Snuggle TeX for on-the-fly as-you-type rendering
All up and ready on the EuDML system
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Searching (semantically) similar papers
Exploration of a DML browsing (semantically) similar papers
Semantic search via topic modeling Latent Semantic Indexing LatentDirichlet Allocation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Leading Edge Example Automated Meaning Picking from Texts
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Probabilistic Topical Modeling Latent Dirichlet Allocation
bull topic weighted list of words
bull document weighted list of topics
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Example
math processing
ordering
tokenization
variables unification
constants unification
indexing
searching
wei
ghtin
g
canonicalization
searchingindexing
x y+y3
x y+y3 x y y3 x y 3+
x y+y3 x y y3 x y 3+ id1id 2+id 2
3 id 1id 2 id 1
3
x y+y3 x y y3 x y 3+ id1id 2+id 2
3
id1id 2 id1
3 x y+ yconst yconst id 1id 2+id 2
const id 1const
x y+y3
x y+y2
x y+y2
x y+y 2 id 1id 2+id 2
2
x y+y 2 id1id 2+id 2
2
x y+yconst id 1id 2+id 2
const
x y+yconst id 1id 2+id 2
const Match
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formula processing example ndash subformulae weighting
(a+b2+c 0125)
(a+bc+2 0125)
(a 00875) (+ 00875) (bc+2 00875)
(b 006125) (c+2 006125)
(c 0042875)(+ 0042875)
(2 0042875)
(id 1+2 00343)
(c+const 0030625)
(id 1+const 001715)
(id 1id 2+2 007)
(bc+const 004375)
(id 1id 2+const 0035)
(id 1+id 2id 3+2 01)
(a+bc+const 00625)
(id 1+id 2id 3+const 005)
input
ordering
tokenization
variablesunification
constantsunification
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Implementation
bull Java
bull Lucene 310 now switching to LuceneSolr 4
bull Mathematical part implements Lucenersquos interface Tokenizer ndash able tointegrate to any Lucene based system
bull MIaS4Solr plugin was created for the use in Solr
bull Textual content ndash processed by StandardAnalyzer
bull easily deployable in JavaLucene based system or as a web service
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Search demonstration
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formulae search demonstration comments
Demo web interface httpaurafimunicz8085webmias
bull MathMLTEX input (Tralics [2] for conversion to MathML [])
bull Canonicalization of the query ndash problems with UMCL library [1]
bull Matched document snippet generation
bull MathJax for nicer math rendering and better portability
bull Snuggle TeX for on-the-fly as-you-type rendering
All up and ready on the EuDML system
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Searching (semantically) similar papers
Exploration of a DML browsing (semantically) similar papers
Semantic search via topic modeling Latent Semantic Indexing LatentDirichlet Allocation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Leading Edge Example Automated Meaning Picking from Texts
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Probabilistic Topical Modeling Latent Dirichlet Allocation
bull topic weighted list of words
bull document weighted list of topics
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formula processing example ndash subformulae weighting
(a+b2+c 0125)
(a+bc+2 0125)
(a 00875) (+ 00875) (bc+2 00875)
(b 006125) (c+2 006125)
(c 0042875)(+ 0042875)
(2 0042875)
(id 1+2 00343)
(c+const 0030625)
(id 1+const 001715)
(id 1id 2+2 007)
(bc+const 004375)
(id 1id 2+const 0035)
(id 1+id 2id 3+2 01)
(a+bc+const 00625)
(id 1+id 2id 3+const 005)
input
ordering
tokenization
variablesunification
constantsunification
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Implementation
bull Java
bull Lucene 310 now switching to LuceneSolr 4
bull Mathematical part implements Lucenersquos interface Tokenizer ndash able tointegrate to any Lucene based system
bull MIaS4Solr plugin was created for the use in Solr
bull Textual content ndash processed by StandardAnalyzer
bull easily deployable in JavaLucene based system or as a web service
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Search demonstration
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formulae search demonstration comments
Demo web interface httpaurafimunicz8085webmias
bull MathMLTEX input (Tralics [2] for conversion to MathML [])
bull Canonicalization of the query ndash problems with UMCL library [1]
bull Matched document snippet generation
bull MathJax for nicer math rendering and better portability
bull Snuggle TeX for on-the-fly as-you-type rendering
All up and ready on the EuDML system
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Searching (semantically) similar papers
Exploration of a DML browsing (semantically) similar papers
Semantic search via topic modeling Latent Semantic Indexing LatentDirichlet Allocation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Leading Edge Example Automated Meaning Picking from Texts
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Probabilistic Topical Modeling Latent Dirichlet Allocation
bull topic weighted list of words
bull document weighted list of topics
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Implementation
bull Java
bull Lucene 310 now switching to LuceneSolr 4
bull Mathematical part implements Lucenersquos interface Tokenizer ndash able tointegrate to any Lucene based system
bull MIaS4Solr plugin was created for the use in Solr
bull Textual content ndash processed by StandardAnalyzer
bull easily deployable in JavaLucene based system or as a web service
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Search demonstration
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formulae search demonstration comments
Demo web interface httpaurafimunicz8085webmias
bull MathMLTEX input (Tralics [2] for conversion to MathML [])
bull Canonicalization of the query ndash problems with UMCL library [1]
bull Matched document snippet generation
bull MathJax for nicer math rendering and better portability
bull Snuggle TeX for on-the-fly as-you-type rendering
All up and ready on the EuDML system
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Searching (semantically) similar papers
Exploration of a DML browsing (semantically) similar papers
Semantic search via topic modeling Latent Semantic Indexing LatentDirichlet Allocation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Leading Edge Example Automated Meaning Picking from Texts
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Probabilistic Topical Modeling Latent Dirichlet Allocation
bull topic weighted list of words
bull document weighted list of topics
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Search demonstration
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formulae search demonstration comments
Demo web interface httpaurafimunicz8085webmias
bull MathMLTEX input (Tralics [2] for conversion to MathML [])
bull Canonicalization of the query ndash problems with UMCL library [1]
bull Matched document snippet generation
bull MathJax for nicer math rendering and better portability
bull Snuggle TeX for on-the-fly as-you-type rendering
All up and ready on the EuDML system
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Searching (semantically) similar papers
Exploration of a DML browsing (semantically) similar papers
Semantic search via topic modeling Latent Semantic Indexing LatentDirichlet Allocation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Leading Edge Example Automated Meaning Picking from Texts
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Probabilistic Topical Modeling Latent Dirichlet Allocation
bull topic weighted list of words
bull document weighted list of topics
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Formulae search demonstration comments
Demo web interface httpaurafimunicz8085webmias
bull MathMLTEX input (Tralics [2] for conversion to MathML [])
bull Canonicalization of the query ndash problems with UMCL library [1]
bull Matched document snippet generation
bull MathJax for nicer math rendering and better portability
bull Snuggle TeX for on-the-fly as-you-type rendering
All up and ready on the EuDML system
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Searching (semantically) similar papers
Exploration of a DML browsing (semantically) similar papers
Semantic search via topic modeling Latent Semantic Indexing LatentDirichlet Allocation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Leading Edge Example Automated Meaning Picking from Texts
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Probabilistic Topical Modeling Latent Dirichlet Allocation
bull topic weighted list of words
bull document weighted list of topics
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Searching (semantically) similar papers
Exploration of a DML browsing (semantically) similar papers
Semantic search via topic modeling Latent Semantic Indexing LatentDirichlet Allocation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Leading Edge Example Automated Meaning Picking from Texts
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Probabilistic Topical Modeling Latent Dirichlet Allocation
bull topic weighted list of words
bull document weighted list of topics
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Leading Edge Example Automated Meaning Picking from Texts
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Probabilistic Topical Modeling Latent Dirichlet Allocation
bull topic weighted list of words
bull document weighted list of topics
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Probabilistic Topical Modeling Latent Dirichlet Allocation
bull topic weighted list of words
bull document weighted list of topics
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Topical Modeling Latent Dirichlet Allocation II
bull all topics computed automatically from document corpora
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Content Similarity Results in lthttpeudmlorggt
We have developed and delivered technology for similarity (gensim)document conversions (to Braille or to text Mathml2text) and math contentnormalization Different formulae representations for similarity computation
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Summary
bull EuDML is up and running with several novel math-aware approachesdeveloped and in use
bull verified complex workflow and proven technologies and tools for DML
bull Scalable solution for math formulae search researched implementedtested and integrated into current version of EuDML system
bull MIRMIaS project pages ndash httpsmirfimunicz
bull math-aware methods for document similarity (MathML2text gensim)
bull a lot more on lthttpprojecteudmlorggt (eg PDF size reduction of 62of original already CCITT-G4 compressed PDFs etc)
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Future work
bull DML workshop series join us at DML 2013 co CICM Bath in UK in July 2013
bull Activities towards WDML (Sloan fundinghellip)
bull EuDML initiative consortium further sustainability solutions (grant proposalwriting)
bull Improved MathML canonicalization and new preprocessing filters searchdeveloped and evaluated with the use of EuDML math query database ofintentions
bull Addition of Content MathML tree indexing
bull NCTIR 11
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Acknowledgments and questions
Acknowledgements EuDML project (funding) ELIAS (trip here) EuDMLcolleagues and authors and contributors of tools used
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Archambault D Moccedilo V Canonical MathML to Simplify Conversion of MathML to Braille Mathematical Notations In
Miesenberger K Klaus J Zagler W Karshmer A (eds) Computers Helping People with Special Needs Lecture Notes inComputer Science vol 4061 pp 1191ndash1198 Springer Berlin Heidelberg (2006) lthttpdxdoiorg10100711788713_172gt
Grimm J Producing MathML with Tralics In Sojka [4] pp 105ndash117 lthttpdmlczdmlcz702579gt
MREC ndash Mathematical REtrieval Collection lthttpnlpfimuniczprojektyeudmlMRECindexhtmlgt
Sojka P (ed) Towards a Digital Mathematics Library Masaryk University Paris France (Jul 2010)
lthttpwwwfimunicz sojkadml-2010-programhtmlgt
Sojka P Liacuteška M Indexing and Searching Mathematics in Digital Libraries ndash Architecture Design and Scalability Issues In
Davenport JH Farmer W Urban J Rabe F (eds) Proceedings of CICM Conference 2011 (CalculemusMKM) Lecture Notes inArtificial Intelligence LNAI vol 6824 pp 228ndash243 Springer-Verlag Berlin Germany (Jul 2011)lthttpdxdoiorg101007978-3-642-22673-1_16gt
Liacuteška Martin and Petr Sojka and Michal Růžička Similarity Search for Mathematics Masaryk University team at the NTCIR-10 Math
T ask In Proceedings of the 10th NTCIR Workshop Meeting on Evaluation of Information Access Technologies Math Pilot Taskpp 686-691 NII Tokyo 2013 PDF
D Formaacutenek M Liacuteška M Růžička and P Sojka Normalization of digital mathematics library content In J Davenport J Jeuring
C Lange and P Libbrecht editors 24th OpenMath Workshop 7th Workshop on Mathematical User Interfaces (MathUI) andIntelligent Computer Mathematics Work in Progress number 921 in CEUR Workshop Proceedings pp 91ndash103 Aachen 2012
Sojka Petr and Martin Liacuteška The Art of Mathematics Retrieval In Matthew R B Hardy Frank Wm Tompa Proceedings of the
2011 ACM Symposium on Document Engineering Mountain View CA USA ACM 2011 p 57ndash60 ISBN 978-1-4503-0863-2lthttpdxdoiorg10114520346912034703gt
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
Overview
Motivation EuDML
Aggregation
Conversions
Search
Similarity
Conclusions
Sylwestrzak W Borbinha J Bouche T Nowiński A Sojka P EuDMLmdashTowards the European Digital Mathematics Library In
Sojka [4] pp 11ndash24 lthttpdmlczdmlcz702569gt
Martin Liacuteška Petr Sojka Michal Růžička and Petr Mravec
Web Interface and Collection for Mathematical RetrievalIn Petr Sojka and Thierry Bouche editors Proceedings of DML 2011 pages 77ndash84 Bertinoro Italy July 2011 Masaryk Universitylthttpdmlczdmlcz702604gt
Credits for LDA pictures goes to David M Blei
Credits for illustrations goes to Jiřiacute Franek
National Institute of Informatics Tokyo June 24th 2013 EuDML AnOverview ofMath Specific Technologies
![oxford [R] Companion for Experimental Design and …clcl/Q560_OLD/R_comp4Abdi_ExperimentalDesign… · Argentina Austria Brazil Chile CzechRepublic France Greece ... Oxford is a registeredtrademarkofOxfordUniversityPress](https://static.fdocuments.us/doc/165x107/5b649e887f8b9af5448d8aa8/oxford-r-companion-for-experimental-design-and-clclq560oldrcomp4abdiexperimentaldesign.jpg)
