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Transcript of The complexity of biodiversity knowledge Andrew C. Jones Cardiff University...
![Page 1: The complexity of biodiversity knowledge Andrew C. Jones Cardiff University Andrew.C.Jones@cs.cardiff.ac.uk Malcolm Scoble The Natural History Museum M.Scoble@nhm.ac.uk.](https://reader035.fdocuments.us/reader035/viewer/2022070307/551a526655034643688b463e/html5/thumbnails/1.jpg)
The complexity of biodiversity knowledge
Andrew C. JonesCardiff University
Malcolm ScobleThe Natural History Museum
![Page 2: The complexity of biodiversity knowledge Andrew C. Jones Cardiff University Andrew.C.Jones@cs.cardiff.ac.uk Malcolm Scoble The Natural History Museum M.Scoble@nhm.ac.uk.](https://reader035.fdocuments.us/reader035/viewer/2022070307/551a526655034643688b463e/html5/thumbnails/2.jpg)
2Jones & Scoble, Semantic Interop., Imperial Coll, 30/03/06
Purpose of talk
• Malcolm & Andrew are both investigators in BiodiversityWorld (BDW)
• There are many problems BDW doesn’t solve yet …
• … and the funding runs out tomorrow!• We’ll present
– BiodiversityWorld as a framework to support biodiversity research
– Other projects in which biodiversity informatics problems have been addressed individually
• Major challenge: draw these disparate efforts together
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4Jones & Scoble, Semantic Interop., Imperial Coll, 30/03/06
Why Biodiversity Informatics is hard
• Need to integrate data & tools of different kinds for interesting “in silico” analyses
• Various computer science issues, e.g.– Human-Computer Interaction
• Design of environments to support scientific research
– Interoperability– Complexity & heterogeneity of data
• Differences of scientific opinion
• Data quality problems
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5Jones & Scoble, Semantic Interop., Imperial Coll, 30/03/06
The BiodiversityWorld project
• 3 year e-Science project funded by BBSRC• Partners: The University of Reading, Cardiff
University, The Natural History Museum, Southampton University
• Aim:– Build a Biodiversity Grid
(Problem Solving Environment to support Biodiversity research)
– Support discovery & use of arbitrary tools & data sources for interesting in silico experiments
– Provide environment to get beyond the ‘cutting and pasting into Word documents’ approach to data integration and analysis
![Page 6: The complexity of biodiversity knowledge Andrew C. Jones Cardiff University Andrew.C.Jones@cs.cardiff.ac.uk Malcolm Scoble The Natural History Museum M.Scoble@nhm.ac.uk.](https://reader035.fdocuments.us/reader035/viewer/2022070307/551a526655034643688b463e/html5/thumbnails/6.jpg)
6Jones & Scoble, Semantic Interop., Imperial Coll, 30/03/06
Example problems for BiodiversityWorld
• How should conservation efforts be concentrated?– (example of Biodiversity Richness & Conservation
Evaluation)• Where might a species be expected to occur,
under present or predicted climatic conditions?– (example of Bioclimatic & Ecological Niche
Modelling)• How can geographical information assist in
selection among possible phylogenetic trees?– (example of Phylogenetic Analysis &
Palaeoclimate Modelling)
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7Jones & Scoble, Semantic Interop., Imperial Coll, 30/03/06
BiodiversityWorld architecture
BiodiversityWorld-GRID Interface (BGI)
The GRID
Workflow enactment
engine Wrapped resources
Native Biodiversity-
World Resources
Metadata repository
Presentation
BGI API
User interface
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10Jones & Scoble, Semantic Interop., Imperial Coll, 30/03/06
Some problems not fully solved in BDW
• Flexible data access– BGI designed to make BDW maintainable, but currently
assumes each resource has a predefined set of operations– BioDA project investigated use of OGSA-DAI in BDW
• HCI issues– A much more exploratory approach to workflow construction
might be appropriate?
• Semantic interoperability & data quality– Metadata repository: basic information only– Only basic solution to species naming problems (SPICE)– Other problems of descriptive terms, differences of expert
opinion, etc., remain to be addressed
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11Jones & Scoble, Semantic Interop., Imperial Coll, 30/03/06
Complexity of biodiversity data: a multi-dimensional problem
• Same specimen might be described with differences of:– Terminology– Opinion about identification– Opinion about whether a particular feature is present– Accuracy
• Experts may differ as to:– Circumscription associated with a given scientific name
• (So may not be describing the same concept)– Terminology used to describe a given taxon– Accepted name for a species in a taxonomic checklist
• There may be errors!• ...
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12Jones & Scoble, Semantic Interop., Imperial Coll, 30/03/06
SPICE for Species 2000
• BBSRC/EPSRC- and EU-funded• SPecies 2000 Interoperability Co-ordination
Environment• Aims:
– build scalable, federated scientific name catalogue organised by taxon (species, etc.)
– provide ‘synonymy server’, enriching information retrieval
• Issue: how to build an architecture to integrate specialist, heterogeneous databases, providing a consistent federated view of broader scope?
• Common Data Model sufficed …– data requirements of federation identical for each database– small set of ‘canned queries’ adequate for the catalogue
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13Jones & Scoble, Semantic Interop., Imperial Coll, 30/03/06
SPICE internal architecture
GSD GSD
Wrapper(e.g. JDBC)
Wrapper(e.g.CGI/XML
+ ODBC)
User(Web Browser)
User(Web browser)……
……
(in some cases, generic) CORBA ‘wrapper’ element of GSD Wrapper
User Server module(HTTP)
‘Query’ co-ordinator
CAS knowledge repository(taxonomic hierarchy, annual checklist, genus
and other caches, ...)
Common Access System (CAS)
CORBA
Internalwrapper
Externalwrapper
XMLCGI
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14Jones & Scoble, Semantic Interop., Imperial Coll, 30/03/06
LITCHI
• BBSRC/EPSRC- and EU- funded• Logic-based Integration of Taxonomic Conflicts in
Heterogeneous Information systems• Aim: detect conflicts between species checklists and either
– Assist in producing a consistent checklist, or– Generate correspondences between checklists (‘cross-map’)
• Addressing problems of species classification & naming variations when accessing species-related data
• More general, semantic interoperability issue:– detecting conflicts between different expert views of same subject
matter;– supporting data access based on these views
![Page 15: The complexity of biodiversity knowledge Andrew C. Jones Cardiff University Andrew.C.Jones@cs.cardiff.ac.uk Malcolm Scoble The Natural History Museum M.Scoble@nhm.ac.uk.](https://reader035.fdocuments.us/reader035/viewer/2022070307/551a526655034643688b463e/html5/thumbnails/15.jpg)
15Jones & Scoble, Semantic Interop., Imperial Coll, 30/03/06
LITCHI example
Checklist 1
– Caragana arborescens Lam. (accepted name)
Caragana sibirica Medikus (synonym)
Checklist 2
– Caragana sibirica Medikus (accepted name)Caragana arborescens Lam. (synonym)
(“Lam.” = “Lamark”)
“A full name which is not a pro-parte name may not appear as both an accepted name and a synonym in the same checklist”
)(_)(_
),,,,(),,,,(_
,,,,,,
21
2211
2121
cparteprocpartepro
tlcansynonymtlcannameaccepted
ttcclan
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16Jones & Scoble, Semantic Interop., Imperial Coll, 30/03/06
Name relationships (LITCHI 2)
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17Jones & Scoble, Semantic Interop., Imperial Coll, 30/03/06
myViews
• Not funded yet – limited proof-of-concept prototype only
• Addresses problem that an expert may wish to generate taxon descriptions which are:– Coherent;– Mapped explicitly to other taxon descriptions, and– Based directly on existing documentation
(monographs, etc), rather than completely re-coded in some restrictive formalism with a new vocabulary
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18Jones & Scoble, Semantic Interop., Imperial Coll, 30/03/06
Example: describing the same things?• Description A:
– Sarothamnus scoparius (L.) Wimm. ex Koch.– Broom– ... a bush which is 50-200 cm high ...
• Description B:– Cytisus scoparius– Yellow broom– ... a small shrub up to 6ft or more ... native in its yellow form ...
• Description C:– Cytisus scoparius (L.) Link.– Broom– ... a deciduous shrub growing to 2.4m by 1m at a fast rate ... scented flowers ...
• Description D:– Common Broom– Cytisus scoparius– ... covered in profuse golden-yellow flowers ... shrub about 1-3m tall ...
• Description E:– Broom– Cytisus scoparius– ... Like a spineless edition of gorse ... with larger scentless flowers ...
• Similar problems apply to individual specimen descriptions
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19Jones & Scoble, Semantic Interop., Imperial Coll, 30/03/06
Things we might want to do
• In a system where– data is held in as ‘raw’ a form as possible, to avoid information loss, but– we can impose various views and hypotheses
we might wish to …
• Create our own ‘view’ of the data– For a given piece of knowledge, we could
• accept it unaltered• accept but re-express in our terms (e.g. different scientific name; different units; ...)• state it is equivalent to another piece of knowledge
(e.g. minor differences in measurements)• flag it as ‘wrong’• ...
– In relation to another’s view, we might• include or ignore it• declare some ‘mapping’ applicable to a group of items
(e.g. every species of ‘Sarothamnus’ is mapped to ‘Cytisus’)• ...
• Reason with differing levels of precision simultaneously (e.g. binary/continuous characters derived from same features)
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20Jones & Scoble, Semantic Interop., Imperial Coll, 30/03/06
An experimental prototype
• Proof of concept ...– arbitrary, small data set from various sources: Cytisus & Genista
species– No real ‘front end’ or ‘back end’ yet!
• Implemented in Prolog (a logic programming language)• Formalisms to record complex assertions & their sources• Ontological knowledge not currently separated out explicitly;
rules perform inference• User makes his/her own assertions about (for example)
– synonymy;– which assertions of others to accept;– ...
• ... both very specific and more general rules• Main purpose: illustrate handling multiple opinions/hypotheses
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21
Sample knowledge base extractsassertion(1, association(2, 3,
absent(scent(flowers)))).assertion(1, property(2, yellow(flowers))).assertion(1, label(2, common('Broom'))).assertion(1, label(2,
species('Cytisus', 'scoparius'))).
assertion(4, property(5, shrublet(whole))).assertion(4, property(5, deciduous(whole))).assertion(4, property(5, size(6, in, whole))).assertion(4, property(5, deep_yellow(flowers))).assertion(4, property(5, small(leaves))).assertion(4, label(5,
species('Cytisus', 'ardoinii'))).
assertion(4, property(7, size(6, ft, whole))).assertion(4, label(7,
species('Cytisus', 'scoparius'))).
assertion(12, label(13, common('Broom'))).assertion(12, label(13,
common('Scotch Broom'))).assertion(12, property(13,
compound('sparteine'))).
assertion(12, property(13, compound('tyramine'))).
assertion(12, label(13,species('Sarothamnus', 'scoparius'))).
assertion(14, label(15,species('Sarothamnus', 'scoparius'))).
assertion(14, property(15,size_range(50, 200, cm, whole))).
assertion(14, property(15, bright_yellow(flowers))).
assertion(16, label(17,species('Cytisus', 'scoparius'))).
assertion(16, property(17,max_height(2.4, m, whole))).
assertion(16, property(17,max_width(1, m, whole))).
assertion(16, property(17, present(scent(flowers)))).
assertion(8, property(9, golden_yellow(flowers))).
assertion(8, property(9,size_range(1, 3, m, whole))).
assertion(8, label(9,species('Cytisus', 'scoparius'))).
Source 12 assertsthat item 13’s
label is commonname ‘Scotch Broom’
![Page 22: The complexity of biodiversity knowledge Andrew C. Jones Cardiff University Andrew.C.Jones@cs.cardiff.ac.uk Malcolm Scoble The Natural History Museum M.Scoble@nhm.ac.uk.](https://reader035.fdocuments.us/reader035/viewer/2022070307/551a526655034643688b463e/html5/thumbnails/22.jpg)
22Jones & Scoble, Semantic Interop., Imperial Coll, 30/03/06
Deducing from the knowledge base?- display_accepted_props('Cytisus', 'ardoinii'). shrublet(whole)deciduous(whole)size(6, in, whole)deep_yellow(flowers)small(leaves)
Yes?- display_accepted_props('Cytisus', 'scoparius').yellow(flowers)size(6, ft, whole)golden_yellow(flowers)size_range(1, 3, m, whole)max_height(2.4, m, whole)max_width(1, m, whole)present(scent(flowers))absent(spines)absent(scent(flowers))
Yes
?- display_contradictions_for('Cytisus', 'scoparius').[present(scent(flowers)), absent(scent(flowers))]
Yes
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23Jones & Scoble, Semantic Interop., Imperial Coll, 30/03/06
Adding synonymy (1)
• User regards any statement about a Sarathamnus species as being a statement about a Cytisus species with same epithet:
• assertion(20,synonym(species('Cytisus', Epithet), _, species('Sarothamnus', Epithet), _)).
• (Could be more restrictive, e.g. apply to only particular information sources)
![Page 24: The complexity of biodiversity knowledge Andrew C. Jones Cardiff University Andrew.C.Jones@cs.cardiff.ac.uk Malcolm Scoble The Natural History Museum M.Scoble@nhm.ac.uk.](https://reader035.fdocuments.us/reader035/viewer/2022070307/551a526655034643688b463e/html5/thumbnails/24.jpg)
24Jones & Scoble, Semantic Interop., Imperial Coll, 30/03/06
Adding synonymy (2)?- display_accepted_props('Cytisus', 'scoparius').yellow(flowers)size(6, ft, whole)golden_yellow(flowers)size_range(1, 3, m, whole)max_height(2.4, m, whole)max_width(1, m, whole)present(scent(flowers))compound(sparteine)compound(tyramine)size_range(50, 200, cm, whole)bright_yellow(flowers)absent(spines)absent(scent(flowers))
Yes?- display_contradictions_for('Cytisus', 'scoparius').[size_range(1, 3, m, whole), size_range(50, 200, cm, whole)][present(scent(flowers)), absent(scent(flowers))]
Yes
![Page 25: The complexity of biodiversity knowledge Andrew C. Jones Cardiff University Andrew.C.Jones@cs.cardiff.ac.uk Malcolm Scoble The Natural History Museum M.Scoble@nhm.ac.uk.](https://reader035.fdocuments.us/reader035/viewer/2022070307/551a526655034643688b463e/html5/thumbnails/25.jpg)
25Jones & Scoble, Semantic Interop., Imperial Coll, 30/03/06
Some important issues for future work
• Complexity, e.g.– Trade-off: effective resource discovery v. computational
expense of traversing rich ontology– Scalability of taxonomic conflict detection
• May find large data sets need clever techniques such as Rete network
– Scalability of inference in myViews; caching inferred information
• Managing & ranking large result sets– How to rank resources discovered– How to rank conflicts
to present users with matches they are likely to want• Joining all these fragmentary projects up together
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27Jones & Scoble, Semantic Interop., Imperial Coll, 30/03/06
Specimen (unit) dataCollection-level
Observations
Locality
Date of specimen collection
Time of specimen collection
Name of collector
Species/taxon concept
Type specimen
Homonyms Author of taxon
Date of description
Genus name(for binomial)
Images
The complexity of taxonomic/biodiversity data
Species name DNA barcodes
Synonyms
Species concepts
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28Jones & Scoble, Semantic Interop., Imperial Coll, 30/03/06
Where we are now
• Fragmented results
• Fragmented effort
• Largely a paper medium (restricted access)
Where we want to be
• Less fragmented; single site or distributed access
• Easier to update• Coordinated effort• Electronic (or dual)
medium• Free access to data• Taxonomy easier to
use
Taxonomy: from a ‘fragmented’ to a ‘distributed’ resource
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29Jones & Scoble, Semantic Interop., Imperial Coll, 30/03/06
Projects to integrate biodiversity data
• BioCISE (collection-level)
• ENHSIN (specimen (unit)-level)
• BioCASE (unit- & collection-level)
• Species 2000 (species nomenclature)
• SYNTHESYS (taxonomic infrastructure)
• ENBI (network of biodiversity information)
• EDIT (distributed approach to taxonomy)
• PBIs (inventorying the planet’s biodiversity)
• CATE: Creating a Taxonomic e-Science
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30Jones & Scoble, Semantic Interop., Imperial Coll, 30/03/06
BioCASE National Node Network
BioCASE National Node
CORM
• 31 National Nodes
• Core Meta Database is updated every night
Collection-level
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31Jones & Scoble, Semantic Interop., Imperial Coll, 30/03/06
NNNNCollection
BioCASE Core
WWWInterface P
Core Data Items
(BioCASE Profile)
keywords,keyword Relations
Enh
ance
dM
eta-
Dat
a
Thesaurus
L
SH
Unitaccess
Metadata
IndexP
B
Cor
e D
ata
Item
s(B
ioC
AS
E P
rofi
le)
L
Collection-levelMeta-Data
X
SpecialInterest
Networks
NationalNodes
NN
UnitInformation
DB
UnitInformation
DB
UnitInformation
DB
UnitInformation
DB
Unit-D
ata
(ABCD)
L,B
All levels
A Biological Collections Service for Europe
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Creating a taxonomic e-science (CATE)
• Literature scattered over 250 years of paper publications.
• Data inaccessible other than to specialist users
• Aim to transfer in toto the taxonomy of two groups of organisms to the web (Hawkmoths and Aroids).
• Broad aim: to encourage migration of taxonomy to the web.
• Provide data for those studying biodiversity.
• Encourage quality control, peer-review and the development of “consensus” taxonomies in the web environment.
• Develop means of citation for web-based revisions
Arisaema candidissimumPhoto : RBG Kew
The Hawkmoth Sphinx caligineus sinicus from Beijing, China.Photo: Tony Pittaway