The InfoQuilt Project THE INFOQUILT VISION Semantic interoperability between systems, sharing...
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The InfoQuilt ProjectThe InfoQuilt Project
THE INFOQUILT VISIONTHE INFOQUILT VISION
Semantic interoperability between systems, sharing knowledge Semantic interoperability between systems, sharing knowledge using multiple ontologies using multiple ontologies
Logical correlation of informationLogical correlation of information
Media independent information processingMedia independent information processing
REALIZATION OF THE VISIONREALIZATION OF THE VISION
fully distributed, adaptable, agent-based systemfully distributed, adaptable, agent-based system
information/knowledgement supported by collaborative information/knowledgement supported by collaborative processes processes
http://lsdis.cs.uga.edu/proj/iq/iq.html
InfoQuilt Project: using the InfoQuilt Project: using the MMetadata etadata REFREFerence linkerence link
http://lsdis.cs.uga.edu/proj/iq/iq.html
MREF MREF
Complements HREF, creating a “logical web” through media Complements HREF, creating a “logical web” through media independent ontology & metadata based correlationindependent ontology & metadata based correlation
It is a description of the information asset we want to retrieveIt is a description of the information asset we want to retrieve
MREFMREF
domain ontologies
IQ_Asset ontology +extension ontologies
attributesrelations
constraints
keywords content attributes(color, scene cuts, …)
Semantic Correlation using MREF MREF Concept
Model for logical
correlation using
ontological terms
and metadata
Framework for
representing MREF’s
Serialization
(one implementation
choice)
X M L
M R E F
R D F
domain specific metadata: terms chosen from domain specific ontologies
Domain Specific Correlation – exampleDomain Specific Correlation – example
Potential locations for a future shopping mall identified by all regionsregions having a
populationpopulation greater than 5000, and areaarea greater than 50 sq. ft. having an urban
land coverland cover and moderate reliefrelief <A MREF ATTRIBUTES(population > 5000; area > 50;
region-type = ‘block’; land-cover = ‘urban’; relief = ‘moderate’) can be viewed here</A>
Population:Area:
Land cover:Relief:
Boundaries:
Census DB TIGER/Line DB US Geological Survey
Regions(SQL):
Boundaries
Image Features (image processing routines)
=> media-independent
relationships between domain
specific metadata: population,
area, land cover, relief
=> correlation between image
and structured data at a
higher domain specific level
as opposed to physical “link-
chasing” in the WWW
A DL II approach for Information BrokeringA DL II approach for Information Brokering
CONSTRUCTING ADDITIONALMETA-INFORMATION RESOURCES
Physical/SimulationWorld
DISCOVERING COLLECTIONS OF HETEROGENEOUS INFORMATION AND
META-INFORMATION RESOURCES
Images Data Stores Documents Digital Media
DomainSpecific
Ontologies
Domain Independent Ontologies
Iscape N
CONSTRUCTING APPROPRIATE INFORMATION LANDSCAPESCONSTRUCTING APPROPRIATE INFORMATION LANDSCAPES
Iscape 1
ADEPT Information Landscape Concept PrototypeADEPT Information Landscape Concept Prototype(a scenario for Digital Earth:
learning in the context of the “El Niño” phenomenon)
Sample Iscapes Requests:
– How does El Niño affect sea animals? Look for
broadcast videos of less than 2 minutes.
– How are some regions affected by El Niño? Look at
East/West Pacific regions.
– What disasters have been related to El Niño?
– What storm occurrences are attributed to El Niño?
– Show reports related to El Niño that contain Clinton.
TRY ISCAPE CONCEPT DEMO
request information using
keywordskeywords
domain-specific attributesdomain-specific attributes
domain-independent attributesdomain-independent attributes
Putting MREFs to workPutting MREFs to work
UserAgent
ProfileManager
user information
MREF request
retri
eve
prof
ile
User
display results
changeprofile
design MREFdomain ontologies
MREF Builder
IQ_Asset ontology +extension ontologies
construct new MREF
Broker Agent
send MREFsend results
retrieve MREF
retrieve MREF
MREFrepository
MREFrepository
Userprofiles
Context: the lynchpin of semanticsContext: the lynchpin of semantics
“For instance, if you were to use Yahoo! or Infoseek to
search the web for pizza, your results would probably
be hundreds of matches for the word pizza. Many of
these could be pizza parlors around the world. Yet if
you run the same search within NeighborNet, you will
allows you to order pizza to be delivered instead of
shipped.”
From a Press Resease of FutureOne, Inc. March 24, 1999
http://home.futureone.com/about/pr/021699.asp
Cricket
Constructing c-contexts from ontological termsConstructing c-contexts from ontological terms
Advantages: Use of ontologies for an intensional
domain specific description of data Representation of extra information
Relationships between objects not represented in the database schema
Using terminological relationships in the ontology
ONTOLOGICAL TERMS
C-CONTEXT:
“All documents stored in the database
have been published by some agency”
=> Cdef(DOC) = <(hasOrganization, AgencyConcept)> C-Context = <(C1 , V1) (C2 , V2) ... (Ck , Vk) >
a collection of
contextual coordinates Ci s (roles) and
values Vi s (concepts/concept descriptions)
AgencyConcept
DATABASEOBJECTS
DocumentConcepthasOrganization
AGENCY(RegNo, Name, Affiliation)
DOC(Id, Title, Agency)
Using c-contexts to reason about Using c-contexts to reason about
information in databaseinformation in database
Cdef(DOC)
<(hasOrganization, AgencyConcept)>
CQ
<(hasOrganization, { “USGS”})>
- Reasoning with c-contexts: glb(Cdef(DOC), CQ)
- Ontological Inferences:
- DocumentConcept
- (hasOrganization, { “USGS” })
Challenge 1: use of multiple ontologies
Challenge 2: estimating the loss of information
EXAMPLEEXAMPLE
glb(Cdef(DOC), CQ)
<(self, DocumentConcept),(hasOrganization, { “USGS” })>
Estimating information loss for multi-ontology basedEstimating information loss for multi-ontology basedquery processing in the OBSERVER/InfoQuilt system query processing in the OBSERVER/InfoQuilt system
OBSERVER architectureOBSERVER architecture
Data Repositories
Mappings
Ontologies
COMPONENT NODE
Data Repositories
Mappings
Ontologies
COMPONENT NODE
Data Repositories
Mappings
OntologyServer
QueryProcessor
UserQuery
Ontologies
USER NODE
InterontologiesTerminologicalRelationships
IRM
IRM NODE
OntologyServer
OntologyServer
QueryProcessor
QueryProcessor
Eduardo Mena (III’98)
Estimating information loss for multi-ontology basedEstimating information loss for multi-ontology basedquery processing in the OBSERVER/InfoQuilt system query processing in the OBSERVER/InfoQuilt system
“Get title and number of pages of books written by Carl Sagan”
Query construction - ExampleQuery construction - Example
Eduardo Mena (III’98)
User ontology: WN
[name pages] for
(AND book (FILLS creator “Carl Sagan”))
Target ontology: Stanford-I
Integrated ontology WN-Stanford-I
[title number-of-pages] for
(AND book (FILLS doc-author-name “Carl Sagan”))
Ontologies sites: http://www.cogsci.princeton.edu/~wn/w3wn.htmlOntologies sites: http://www.cogsci.princeton.edu/~wn/w3wn.html
http://www-ksl.stanford.edu/knowledge-sharing/ontologies/html/bibliographic-data/http://www-ksl.stanford.edu/knowledge-sharing/ontologies/html/bibliographic-data/
Estimating information loss for multi-ontology basedEstimating information loss for multi-ontology basedquery processing in the OBSERVER/InfoQuilt system query processing in the OBSERVER/InfoQuilt system
“Get title and number of pages of books written by Carl Sagan”
Query construction - ExampleQuery construction - Example
Eduardo Mena (III’98)
User ontology: WN
[name pages] for
(AND book (FILLS creator “Carl Sagan”))
Target ontology: Stanford-I
Integrated ontology WN-Stanford-I
[title number-of-pages] for
(AND book (FILLS doc-author-name “Carl Sagan”))
Ontologies sites: http://www.cogsci.princeton.edu/~wn/w3wn.htmlOntologies sites: http://www.cogsci.princeton.edu/~wn/w3wn.html
http://www-ksl.stanford.edu/knowledge-sharing/ontologies/html/bibliographic-data/http://www-ksl.stanford.edu/knowledge-sharing/ontologies/html/bibliographic-data/
Biblio-Thing
Document
Book
Edited-Book
Technical-Report
Periodical-Publication
Journal
Magazine
Newspaper
Miscellaneous-Publication
Technical-Manual
Computer-Program
Multimedia-DocumentArtwork
Cartographic-Map
Thesis
Doctoral-Thesis
Master-Thesis
Proceedings
Conference Agent
PersonAuthor Organization
Publisher University
Re-use of Knowledge:Bibliography Data Ontology
Re-use of Knowledge:Bibliography Data OntologyStanford-I
Estimating information loss for multi-ontology basedEstimating information loss for multi-ontology basedquery processing in the OBSERVER/InfoQuilt system query processing in the OBSERVER/InfoQuilt system
“Get title and number of pages of books written by Carl Sagan”
Query construction - ExampleQuery construction - Example
Eduardo Mena (III’98)
User ontology: WN
[name pages] for
(AND book (FILLS creator “Carl Sagan”))
Target ontology: Stanford-I
Integrated ontology WN-Stanford-I
[title number-of-pages] for
(AND book (FILLS doc-author-name “Carl Sagan”))
Ontologies sites: http://www.cogsci.princeton.edu/~wn/w3wn.htmlOntologies sites: http://www.cogsci.princeton.edu/~wn/w3wn.html
http://www-ksl.stanford.edu/knowledge-sharing/ontologies/html/bibliographic-data/http://www-ksl.stanford.edu/knowledge-sharing/ontologies/html/bibliographic-data/
Re-use of Knowledge:A subset of WordNet 1.5Re-use of Knowledge:
A subset of WordNet 1.5Print-Media
Press Publication Journalism
Newspaper MagazineBook
Periodical
Trade-Book Brochure TextBook
Reference-BookSongBook
PrayerBook
PictorialSeries
Journals
CookBook
Instruction-BookWordBook HandBook Directory Annual
Encyclopedia
Manual Bible GuideBook
Instructions Reference-Manual
Estimating information loss for multi-ontology basedEstimating information loss for multi-ontology basedquery processing in the OBSERVER/InfoQuilt system query processing in the OBSERVER/InfoQuilt system
“Get title and number of pages of books written by Carl Sagan”
Query construction - ExampleQuery construction - Example
Eduardo Mena (III’98)
User ontology: WN
[name pages] for
(AND book (FILLS creator “Carl Sagan”))
Target ontology: Stanford-I
Integrated ontology WN-Stanford-I
[title number-of-pages] for
(AND book (FILLS doc-author-name “Carl Sagan”))
Ontologies sites: http://www.cogsci.princeton.edu/~wn/w3wn.htmlOntologies sites: http://www.cogsci.princeton.edu/~wn/w3wn.html
http://www-ksl.stanford.edu/knowledge-sharing/ontologies/html/bibliographic-data/http://www-ksl.stanford.edu/knowledge-sharing/ontologies/html/bibliographic-data/
WN ontology and user query
Estimating information loss for multi-ontology basedEstimating information loss for multi-ontology basedquery processing in the OBSERVER/InfoQuilt system query processing in the OBSERVER/InfoQuilt system
Estimating the loss of informationEstimating the loss of information
Eduardo Mena (III’98)
To choose the plan with the least loss
To present a level of confidence in the answer
Based on intensional information (terminological difference)
Based on extensional information (precision and recall)
Plans in the examplePlans in the example User Query: (AND book (FILLS doc-author-name “Carl Sagan”))
Plan 1: (AND document (FILLS doc-author-name “Carl Sagan”))
Plan 2: (AND periodical-publication (FILLS doc-author-name “Carl Sagan”))
Plan 3: (AND journal (FILLS doc-author-name “Carl Sagan”))
Plan 4: (AND UNION(book, proceedings, thesis, misc-publication, technical-report)
(FILLS doc-author-name “Carl Sagan”))
Estimating information loss for multi-ontology basedEstimating information loss for multi-ontology basedquery processing in the OBSERVER/InfoQuilt system query processing in the OBSERVER/InfoQuilt system
Loss of information based on intensional informationLoss of information based on intensional information
Eduardo Mena (III’98)
User Query: (AND book (FILLS doc-author-name “Carl Sagan”))
Plan 1:
(AND document (FILLS doc-author-name “Carl Sagan”))
book:=(AND publication (AT-LEAST 1 ISBN))
publication:=(AND document (AT-LEAST 1 place-of-publication))
Loss: “Instead of books written by Carl Sagan, OBSERVER is
providing all the documents written by Carl Sagan (even if they
do not have an ISBN and place of publication)”
Estimating information loss for multi-ontology basedEstimating information loss for multi-ontology basedquery processing in the OBSERVER/InfoQuilt system query processing in the OBSERVER/InfoQuilt system
Example: loss for the plansExample: loss for the plans
Eduardo Mena (III’98)
Plan 1: (AND document (FILLS doc-author-name “Carl Sagan”)) [case 2]
91.57% < (1-Loss) < 91.75%
Plan 2: (AND periodical-publication (FILLS doc-author-name “Carl Sagan”))
94.03% < (1-Loss) < 100% [case 3]
Plan 3: (AND journal (FILLS doc-author-name “Carl Sagan”)) [case 3]
98.56% < (1-Loss) < 100%
Plan 4: (AND UNION(book, proceedings, thesis, misc-publication, technical-
report) (FILLS doc-author-name “Carl Sagan”)) [case 1]
0% < (1-Loss) < 7.22%
Summary Summary
TextTextStructured DatabasesStructured Databases DataData Syntax,Syntax,
SystemSystem Federated DBFederated DB
Semi-structuredSemi-structured MetadataMetadata Structural,Structural,SchematicSchematic
Mediator,Mediator,Federated ISFederated IS
Visual,Visual,Scientific/Eng.Scientific/Eng. KnowledgeKnowledge SemanticSemantic
Knowledge Mgmt.,Knowledge Mgmt.,InformationInformationBrokering,Brokering,
Cooperative ISCooperative IS
Agenda for research Agenda for research
Interoperation not at systems level, but at informational and
possibly knowledge level
– traditional database and information retrieval solutions
do not suffice
– need to understand context; measures of similarities
Need to increase impetus on semantic level issues involving
terminological and contextual differences, possible
perceptual
or cognitive differences in future
– information systems and humans need to cooperate,
possible involving a coordination and collaborative
processes
http://lsdis.cs.uga.eduhttp://lsdis.cs.uga.edu[See publications on Metadata, Semantics,Context, [See publications on Metadata, Semantics,Context, InfoHarness/InfoQuilt]InfoHarness/InfoQuilt]
[email protected]@cs.uga.edu
Acknowledgements:Acknowledgements:Tarcisio LimaTarcisio Lima
Vipul KashyapVipul Kashyap
Related ReadingRelated Reading
Books: Information Brokering for Digital Media, Kashyap and Sheth, Kluwer,
1999 (to appear)
Multimedia Data Management: Using Metadata to Integrate and Apply
Digital Media, Sheth and Klas Eds, McGraw-Hill, 1998
Cooperative Information Systems, Papazoglou and Schlageter Eds.,
Academic Press, 1998
Management of Heterogeneous and Autonomous Database Systems,
Elmagarmid, Rusinkiewica, Sheth Eds, Morgan Kaufmann, 1998.
Special Issues and Proceedings: Formal Ontologies in Information Systems, Guarino Ed., IOS Press, 1998
Semantic Interoperability in Global Information Systems, Ouksel and
Sheth, SIGMOD Record, March 1999.