Ontologies: What, Why, and How?

1. Ontologies: What, Why, and How? Jon Corson-Rikert, Mann Library Metadata Working Group 4/18/03

2. What problems are we trying to solve? • Problems with content • Inconsistency • Incompatibility • Incompleteness • Unboundedness • Need for Automation • Discovery • Filtering • Assembly • Interoperability

3. Why consider ontologies? • Sharing common understanding of the structure of information among people or software agents • Codifying domain assumptions • Terminology • Relationships • Reuse of domain knowledge • Improving information retrieval success • Augmenting or refining search terms • Preferred terminology • Discriminating among alternative meanings (e.g., WordNet) • Language translation • Bridging across domains

4. Pre- Web Web Semantic Web Books, Magazines, Articles, …. Books, Magazines, Articles Databases, Webpages Defined Electronic Information Elements Libraries/Archives/File systems Libraries/Archives/File Systems/Websites Electronic Repositories Bibliographic Catalogues on Cards or Computers Bibliographic Catalogues Machine Index Catalogues Machine Readable Metadata Repositories Human Indexing Human Indexing Machine Indexing Machine Indexing Human Indexing Human reading, checking and classifying Statistical Analysis by Machines Semantical Analysis by Machines Bibliographies Bibliographies/Output from Fulltext Search Engines Knowledge based specialized webportals Reviews Knowledge Mining Thesauri, Classification Schemes, Glossaries, Ontologies The Evolution of Knowledge Management Johannes Keizer, FAO

5. What is an ontology? - 1 A thesaurus on steroids • Ordered terminology • Prescribed relationships among terms

6. What is an ontology? - 2 A shallow classification of basic categories • Defines categories, and hence terminology • Defines rules (Soergel 1999)

7. What is an ontology? - 3 In information science: A characterization, through formal, explicit knowledge, of the intended meanings and relationships of a vocabulary of concepts (Gruber 1993)

8. What is an ontology? - 4 A formal explicit description of concepts in a domain of discourse (classes, or concepts), with properties of each concept describing various features and attributes of the concepts (slots, roles, or properties) and restrictions on slots (facets). An ontology together with a set of individual instances of classes constitutes a knowledge base (Ontology 101)

9. Ontologies have … Concepts Relations between concepts • Synonyms • Class/subclass (broader/narrower; dog is to mammal) • Membership (“is a”: Spot is a dog ) • Part/whole (hand is part of arm, car has fender) • Inverse (e.g., pest damages plant so plant is damaged by pest) Axioms (properties and attributes of concepts) • Definitions specifying both necessary and sufficient criteria for membership • Constraints such as domain and range, minimum or maximum number of values

10. Ontologies will (eventually) support: Automatic classification and query • Where does a target word or phrase fit into the ontology • Locating a concept or a cluster of concepts based on a description and/or relationships • Vocabulary switching between domains Inference • Using relationships to determine, given A and B, what C might be and how you know it • Analysis to enhance navigation Consistency checking

11. From common data to common structure • Controlled vocabulary • Very simple structure (nearly flat) • The terms are the data • Taxonomy • Primarily to define position within a hierarchy – e.g., species • Thesaurus • More options for relationships • Often leverages retrieval and organization of additional data • Meta-thesaurus • A federation of similar thesaurus structures to allow bridging data across languages or across domains • Ontology • Whatever can’t be done by the above

12. Typical thesaurus implementation • A controlled vocabulary or thesaurus limited to the domain • A set of separate database tables, each with predictable attributes • People • Departments • Resources • Thesaurus cross-references this content for internal navigation • Incoming keyword queries can provide a rich context of links to data tables

13. Publications People Thesaurus Projects Crops Orgs Genes Website with thesaurus Queries http://mcknight.ccrp.cornell.edu

14. 2nd thesaurus Input query thesaurus Refinement 3rd thesaurus then search against data warehouse Thesaurus as leveraging agent

15. Gazetteer as leveraging agent Scenario: • User finds library record (e.g., book or photo) with place name reference (e.g., neighborhood in L.A.) • Place name and desired action sent to gazetteer (e.g., find other photos in nearby L.A. neighborhoods using appropriate historical neighborhood names) • Gazetteer matches incoming place name with coordinate footprint • Other place names near footprint and in L.A. retrieved • Records related to neighboring places returned to user Requires: • Structured data (place names, coordinates) • Relationships (historical to modern names, neighborhoods to city) • Functionality (coordinate-based spatial analysis)

16. Agriculture Heritage Project • Wide variety of content from diverse organizations • Open-ended content • Time and place as first-order variables • Data likely to cluster by theme, time, and place • Many areas with sparse data • Need to appeal to diverse audiences • Need to produce independently functional results • Goal: transform flat archives into dynamic context of people, places, and events

17. Approach • Simple underlying content model • Adaptive relationships among content • Sometimes very detailed • Often very general • Approachable from any viewpoint • Time, space, originating organization, historical event, personalities, crops, thematic interests • Capability for encapsulation and export as curricular units

18. The ABC Ontology Model • A rich model incorporating time, place, and events as well as information more traditionally encoded in metadata • Designed for exchange and interoperability as RDF-XML metadata • A set of generalized classes and canonical relationships among them • An ontology framework independent of the data it accompanies

19. ABC Ontology classes Entity abstraction actuality temporality time place work agent artifact action event situation manifestation item

20. ABC Ontology diagrams - 1 Events precede or follow situations publication acquisition creation EV0 ST0 EV1 ST1 EV2

21. ABC Ontology diagrams - 2 Most agents, actions, times, and places modify events publication acquisition creation EV2 EV0 ST0 EV1 ST1 hasAction photo published photo taken AC1 AC0 inPlace hasAgent AG1 atTime AG0 photographer publishing house

22. ABC Ontology diagrams - 3 collection Manifestations exist in situations MN3 isPartOf Tulips color print WK0 MN1 Kodak archive poster hasRealization MN2 MN0 color transparency original instanceOf hasRealization the photo the poster publication acquisition creation contains contains EV0 ST0 EV1 ST1 EV2

23. Complete ABC diagram Source: http://metadata.net/harmony/cimi_modelling.htm

24. Source: http://metadata.net/harmony/cimi_modelling.htm

25. ABC class-property relationships • Set of canonical relationships • All bi-directional (inverses) • Provide a domain of possible connections • Serve as the basis for model traversal

26. ABC class-property relationships - 1 Entity-Entity contains - isPartOf Entity-Place inPlace - isLocationOf Actuality-Actuality hasPhase - isPhaseOf Actuality-Situation inContext - isContextFor Work-Manifestation hasRealization - isRealizationOf Manifestation-Item hasCopy - isCopyOf

27. ABC class-property relationships - 2 Temporality-Agent hasParticipant - isParticipant Temporality-Actuality involves - isInvolvedIn transforms - isTransformedBy usesTool – usedAsToolIn destroys - isDestroyedBy hasResult - isResultOf creates – isCreatedBy Event-Action hasAction – isActionOf Event-Agent hasPresence – isPresentIn Situation-Event precedes - isPrecededBy follows - isFollowedBy

28. Work in progress Demo of Agriculture Heritage site prototype

29. Is it worth it? • It’s worth exploring • Must be easier to build • Useful to rethink typical site structure • Not clear how to leverage all the potential power • Need more use cases • What does it mean for metadata?

30. References • “Indirect geospatial referencing through place names in the digital library: Alexandria Digital Library experience with developing and implementing gazetteers,” Linda L. Hill, Zi Zheng, Proceedings of the American Society for Information Science Annual Meeting, Washington, D.C., Oct. 31- Nov. 4, 1999, pp. 57-69. • “Ontology Development 101: A Guide to Creating Your First Ontology”, Natalya F. Noy, Deborah L. McGuinness, Stanford University, Stanford, CA 94305 • “Science and the Semantic Web,” James Hendler, Science, vol. 299, 1/24/03 • “The ABC Ontology and Model,” Carl Lagoze and Jane Hunter, Journal of Digital Information, volume 2 issue 2, November, 2001. • “The Rise of Ontologies or the Reinvention of Classification,” Dagobert Soergel, Journal of the American Society for Information Science, 50(12):1119-1120, 1999 • “Toward Principles for the Design of Ontologies Used for Knowledge Sharing,” Thomas R. Gruber, Revision: August 23, 1993, Stanford Knowledge Systems Laboratory