Ontologies

1. Ontologies

2. What are ontologies? • originally, the filed dedicated to study the nature of everything • sometimes referred to simply as knowledge bases • aim to provide a common language to support knowledge sharing • systems that implements tasks that use knowledge (and thus somehow perform knowledge sharing) should all guarantee a common language

3. From K. McCain presentation March, 2002 WHAT IS AN ONTOLOGY?

4. Ontologies in AI • Artificial intelligence researchers have adopted ontologies as a comprehensive knowledge representation formalism to provide commonsense reasoning in support of knowledge tasks such as knowledge acquisition and reuse. • Lenat DB (1976) AM: An Artificial Intelligence Approach to Discovery in Mathematics as Heuristic Search. PhD thesis, Stanford University

5. What are ontologies (in AI)? “Ontologies are explicit specifications of conceptualizations.” most cited definition from Gruber (1993)

6. What are ontologies (in AI)? Ontologies are explicit descriptions of shared conceptualization: • explicit • descriptions • shared • conceptualization

7. 1. explicit • Has to be explicitly defined through descriptions • types and constraints are explicitly defined

8. 2. descriptions • concepts (or classes) in a domain • properties of each concept describing various features and attributes • and restrictions on the attributes (facets)

9. 3. shared • Common to members of a domain/field • Consensual knowledge • not private to one individual, accepted by a group

10. 4. conceptualization • Interpreted concepts • conceptual (abstract) model of a domain through its relevant concepts

11. Types of Information • concepts, atomic types • cardinality of constraints • is-a hierarchy among concepts • relationships between concepts • taxonomies of relations • reified statements • axioms • semantic entailments

12. From K. McCain presentation March, 2002 Universal Semantic Relationships • STRICT INCLUSION – X is a kind of Y • SPATIAL – X is a place in Y; X is a part of Y • CAUSE-EFFECT – X is a result of Y; X is a cause of Y • RATIONALE – X is a reason for doing Y • LOCATION FOR ACTION – X is a place for doing Y • FUNCTION – X is used for Y • MEANS-END – X is a way to do Y • SEQUENCE – X is a step (stage) in Y • ATRTRIBUTION – X is an attribute (characteristic) of Y From Spradling, The Ethnographic Interview

13. Types of Ontologies • Domain • Additional specializations are possible • applications, tasks • Linguistic • Account for grammar and meanings in a natural language e.g., WordNet for American English

14. Uses of domain ontologies • interoperability among information systems • semantic web: link, coordinate software agents • sharing knowledge bases among KBS • intelligent retrieval, search

15. Why ontologies for KBS? • identify specific classes of objects and relations that exist in some specific domain • need to understand and share common concepts of a domain by different systems • multiple systems can use one same ontology and exchange knowledge and information without conflict • ontological analysis captures the intrinsic conceptual structure of a domain and is the essential step in building coherent knowledge bases • represent facts (propositions) in a domain by combining terms and concepts • represent attitudes, e.g., hypothesize, believe, expect, hope, desire, fear, predicts, plans

16. Ontologies vs. knowledge bases • An ontology together with a set of individual instances of classes constitutes a knowledge base • ontology is a basic structure around which a KB can be built • knowledge bases represents what is true about a domain by using terms and concepts defined in the ontologies • building an ontology implies that the representation language and the ontological analysis can be reused

17. Ontologies supporting NLU • natural language understanding • Syntactic Analysis (Parsing) • Semantic Analysis • Pragmatic Analysis • natural language interfaces • information systems, Artificial Intelligence systems • search engines • machine translation • developing an ontology with ML from a collection of documents and the end-user to support domain-independent information extraction (IJCAI 01)

18. ES methodology Knowledge acquisition working memory (short-term mem/information) expertproblem knowledgebase (e.g.,framesand methods) userI n t e r f a c e inferenceengine (agenda) expertsolution explanation generalknowledge

19. ES supported by ontologies Knowledge acquisition workingmemory expertproblem knowledgebase (e.g.,framesand methods) userI n t e r f a c e inferenceengine explanation expertsolution application specific top level domain specific natural language

20. Ontologies supporting ES top level reasoning natural language application specific domain specific expert problem expert solution

21. top level natural language application specific domain specific ontologies supporting CBR

22. Development steps • Determine the domain and scope • Consider reusing existing ontologies • Enumerate important terms in the ontology • Define the classes and the class hierarchy • Define the attributes of classes (slots) • Define the facets of the slots • Create instances from Noy & McGuinness

23. determinescope considerreuse considerreuse considerreuse enumerate terms enumerate terms defineclasses defineclasses defineclasses defineclasses defineproperties defineproperties defineproperties defineconstraints defineconstraints createinstances createinstances createinstances Ontologies development process • ontology development is an iterative process from Noy & McGuinness

24. Some challenges • hugeness • amount of knowledge is overwhelming • interaction • nature of problem to be solved • type of inference strategy to use • multiple views • difficult consensus • dynamic world • reorganization, maintenance • context (not represented) • all impact on construction, reusability, interfacing

25. Ontology editors(development environments) • ONTOLINGUA http://ontolingua.nici.kun.nl • WEBONTO* http://kmi.open.ac.uk/projects/webonto/ • PROTEGEWIN http://smi-web.stanford.edu/projects/prot-nt/ • ONTOSAURUS* http://www.isi.edu/isd/ontosaurus.html • ODE • KADS22 Duineveld, A.J., Stoter, R., Weiden, M.R., Kenepa, B. and Benjamins, V.R. (2000). WonderTools? A comparative study of ontological engineering tools. International Journal of Human-Computer Studies 52(6): 1111-1133.

26. Duineveld et al.,2000

27. Looking at some ontologies • Open University • http://kmi.open.ac.uk/projects/webonto/ • http://www.isi.edu/isd/ontosaurus.html • USC/Information Sciences Institute

28. These slides were built mainly based on: • Noy, Natalya F. and McGuinness, Deborah L. . Ontology Development 101: A Guide to Creating Your First Ontology. Stanford Knowledge Systems Laboratory Technical Report KSL-01-05 and Stanford Medical Informatics Technical Report SMI-2001-0880, March 2001. Online:http://protege.stanford.edu/publications/ ontology_development/ontology101.html • B. Chandrasekaran, John R. Josephson, and V. Richard Benjamins What Are Ontologies, and Why Do We Need Them? Intelligent Systems & their applications Vol. 14, No. 1, January/February 1999 • Tautz, C. empolis

29. Further Reading • van Heijst, G. (1995) The Role of Ontologies in Knowledge Engineering, PhD thesis, University of Amsterdam.

30. Bibliography (i) 1          Introductory Chandrasekaran, B.; Josephson, John R. and Benjamins, V. Richard. What Are Ontologies, and Why Do We Need Them? Intelligent Systems & their applications Vol. 14, No. 1, January/February 1999 . Gruninger, M. and Lee, Jintae. Ontology Applications and Design. Guest Editors. Communications of the ACM, Vol. 45, No. 2 February, 2002. Guarino, N. and Poli, R. The role of ontology in the information technology. Int’l J. Human-Computer Studies, Vol. 43, Nos. 5/6, Nov-Dec. 1995, pp. 623-965. Heijst, G. van Schreiber, A. Th. and Wielinga, B. J. Using explicit ontologies for KBS development. International Journal of Human-Computer Studies, 46(2/3):183-292, 1997. Swartout, William and Tate, Austin Guest Editors' Introduction: Ontologies Intelligent Systems & their applications Vol. 14, No. 1, January/February 1999

31. Bibliography (ii) Gruber, T. A translational approach to portable ontologies. Knowledge acquisition, vol. 5, no.2, 1993, pp. 199-220. Tautz, C. Tutorial on Practical Ontology Construction. ,Bertelsmann Mohn Media Group, empolis, Germany (unpublished slides). 2          Applications Andre Valente, Thomas Russ, Robert MacGregor, and William Swartout Building and (Re)Using an Ontology of Air Campaign Planning Intelligent Systems & their applications Vol. 14, No. 1, January/February 1999 Mariano Fernández López, Asunción Gómez-Pérez, Juan Pazos Sierra, and Alejandro Pazos Sierra Building a Chemical Ontology Using Methontology and the Ontology Design Environment Intelligent Systems & their applications Vol. 14, No. 1, January/February 1999 Gleb Frank, Adam Farquhar, and Richard Fikes Building a Large Knowledge Base from a Structured Source Intelligent Systems & their applications Vol. 14, No. 1, January/February 1999

32. Bibliography (iii) 2.1        Ontologies and Knowledge Management V.R. Benjamins, D. Fensel, A. Gómez Pérez  link Knowledge Management through Ontologies  Ulrich Reimer (ed.) PAKM 98 Practical Aspects of Knowledge Management. Proceedings of the Second International Conference  Basel, Switzerland, October 29-30, 1998. Motta,Enrico; Shum, Simon Buckingham and Domingue, John. Ontology-Driven Document Enrichment: Principles, Tools and Applications. International Journal of Human-Computer Studies, 52, (6), 1071-1109. O’Leary, D. E. (1998). Using AI in Knowledge Management: Knowledge Bases and Ontologies. Intelligent Systems, 13, 3, pp. 34-39. 2.1.1        Web portals Staab, S.; Jürgen, A.; Decker, S.; Erdmann, E.; Hotho, A.; Maedche, A.; Schnurr, H.P.; Studer, R.; Sure, Y. (2000). AI for the Web - Ontology-based Community Web Portals. Proceedings of the 17th National Conference on Artificial Intelligence and 12th Innovative Applications of Artificial Intelligence Conference, AAAI 2000/IAAI 2000, Menlo Park/CA, Cambridge/MA, AAAI Press/MIT Press.

33. Bibliography (iv) 3. Design and development of ontologies Benjamin, J., Borst, P., Akkermans, J., & Wielinga, B. (1996). Ontology construction for technical domains. In Shadbolt, N., editor, Proceedings 9th European Knowledge Acquisition Workshop EKAW'96, pages 98-114, Berlin. Springer-Verlag. Lecture Notes in Artificial Intelligence No. 1076. Borst,P.,Akkermans,H. and Top,J., Engineering Ontologies, International Journal of Human-Computer Studies, 46:365-406, 1997 Gómez-Pérez, A.; Fernandez, M.; De Vicente, A. Towards a Method to Conceptualize Domain Ontologies. Workshop on Ontological Engineering. ECAI'96. 1996. Pags. 41-51. Gruber, T. (1995). Toward principles for the design of ontologies used for knowledge sharing. International Journal of Human-Computer Studies, 43:907-928.

34. Bibliography (v) Noy, Natalya F. and McGuinness, Deborah L. . Ontology Development 101: A Guide to Creating Your First Ontology. Stanford Knowledge Systems Laboratory Technical Report KSL-01-05 and Stanford Medical Informatics Technical Report SMI-2001-0880, March 2001. Online:http://protege.stanford.edu/publications/ ontology_development/ontology101.html 3.1        Ontology editors Duineveld, A.J., Stoter, R., Weiden, M.R., Kenepa, B. and Benjamins, V.R. (2000). WonderTools? A comparative study of ontological engineering tools. International Journal of Human-Computer Studies 52(6): 1111-1133. Farquhar,A. Fikes,R. and Rice, J. The Ontolingua Server: a Tool for Collaborative Ontology Construction; Intl. Journal of Human-Computer Studies 46, 1997.

35. Bibliography (vi) 4          Learning and acquisition of ontologies Frank,Gleb; Farquhar, Adam and Fikes, Richard Building a Large Knowledge Base from a Structured Source Intelligent Systems & their applications Vol. 14, No. 1, January/February 1999 5          Ontologies and knowledge-based systems van Heijst, G.  (1995). The Role of Ontologies in Knowledge Engineering. PhD thesis, University of Amsterdam. 5.1.1        Ontologies and CBR systems Díaz-Agudo, B. & González-Calero, P.A. (2000). An architecture for knowledge intensive CBR systems. Proceedings of the Fifth European Workshop on Case-Based Reasoning (pp. 37-48). Munich: Springer.