Bertram Lud ä scher LUDAESCH@SDSC.EDU

1. Department of Computer Science & Engineering University of California, San DiegoCSE-291: Ontologies in Data IntegrationSpring 2003 Bertram Ludäscher LUDAESCH@SDSC.EDU

2. Outline • Wrapping up last week • What is a representation? • [Thesauri, Topic Maps] • Predicate Logic Primer • Description logics • [RDF & RDF Schema] • [F-logic] • Topic Selection Special thanks: • Alexander Maedche, Steffen Staab: • ECAI’2002 Tutorial on Ontologies

3. Lack of a shared understanding leads to poor communication => People, organizations and software systems must communicate between and among themselves Disparate modeling paradigms, languages and software tools limit => Interoperability => Knowledge sharing & reuse Ontologies … For What? [Uschold, Gruninger, 96]

4. Origin and History (I) • Ontology .... a philosophical discipline, branch of philosophy that deals with the nature and the organisation of reality • Science of Being (Aristotle, Metaphysics, IV, 1) • Tries to answer the questions: What is being? What are the features common to all beings?

5. Concept “Jaguar“ [Ogden, Richards, 1923] Origin and History (II) • Humans require words (or at least symbols) to communicate efficiently. The mapping of words to things is only indirect possible. We do it by creating concepts that refer to things. • The relation between symbols and things has been described in the form of the meaning triangle:

6. Origin and History (III) • In recent years ontologies have become a hot topic of interest. • Here, an ontology refers to an engineering artifact: • It is constituted by a specific vocabulary used to describe a certain reality, plus • a set of explicit assumptions regarding the intended meaning of the vocabulary. • Thus, ontologies describe a formal partial specification of a specific domain: • Shared understanding of a domain of interest • Formal and machine executeable model of a domain of interest

7. Human and machine communication (I) [Maedche et al., 2002] • ... Human Agent 1 Human Agent 2 Machine Agent 1 Machine Agent 2 exchange symbol, e.g. via nat. language exchange symbol, e.g. via protocols Ontology Description Symbol ‘‘JAGUAR“ Formal Semantics Formal models Internal models commit commit Concept Meaning Triangle MA1 MA2 HA2 HA1 commit Ontology commit a specific domain, e.g. animals Things

8. Ontology & Natural Language • It is important to emphasize that there is a m:n relationship between words and concepts • This means practically: • different words may refer to the same concept • a word may refer to several concepts • Ontologies languages should provide means for making this difference explicit.

9. Lexicon: LC ={person, employee, organisation}, LR ={works at} F(person) = c1, F(employee) = c2, F(organisation) = c3, G(works at) = r1 ... organisation c3 works at .. c1 person r1(c2,c3), .. .. employee HC(c2,c1) c2 Example Ontology: C = {c1,c2,c3}, R = {r1}, HC(c2,c1), r1(c2,c3),

10. Ontology vs. Knowledge Bases • There is no clear separation between ontology and knowledge base • Example: • Often it remains a modeling decision if something is modeled as concept or as instance. In many applications meta-modeling means are required.

11. Types of Ontologies (I) [Guarino, 98] describe very general concepts like space, time, event, which are independent of a particular problem or domain. It seems reasonable to have unified top-level ontologies for large communities of users. describe the vocabulary related to a generic domain by specializing the concepts introduced in the top-level ontology. describe the vocabulary related to a generic task or activity by specializing the top-level ontologies. These are the most specific ontologies. Concepts in application ontologies often correspond to roles played by domain entities while performing a certain activity.

12. Ontologies and their Relatives (I) • There are many relatives around: • Controlled vocabularies, thesauri and classification systems available in the WWW, see http://www.lub.lu.se/metadata/subject-help.html • Classification Systems (e.g. UNSPSC, Library Science, etc.) • Thesauri (e.g. Art & Architecture, Agrovoc, etc.) • Lexical Semantic Nets • WordNet, see http://www.cogsci.princeton.edu/~wn/ • EuroWordNet, see http://www.hum.uva.nl/~ewn/ • Topic Maps, http://www.topicmaps.org (e.g. used within knowledge management applications) • In general it is difficult to find the border line!

13. Ontologies and their Relatives (II) General logical constraints Formal Is-a Thesauri Frames Catalog / ID Informal Is-a Formal Instance Value Restric- tions Terms/ Glossary Axioms Disjoint Inverse Relations, ...

14. Some Ontologies (and Friends) in Action (coming soon to a project near you)

15. Midatlantic Region Rocky Mountains GEON Architecture

16. SMART (Meta)data I: Logical Data Views Adoption of a standard (meta)data model => wrap data sets into unified virtual views Source: NADAM Team (Boyan Brodaric et al.)

17. “smart discovery & querying” via multiple, independent concept hierarchies (controlled vocabularies) • data at different description levels can be found and processed SMART Metadata II: Multihierarchical Rock Classification for “Thematic Queries” (GSC) –– or: Taxonomies are not only for biologists ... Genesis Fabric Composition Texture

18. Biomedical Informatics Research Network http://nbirn.net SMART Metadata III:Source Contextualization & Ontology Refinement Focused GEON ontology working meeting last week ... (GEON, SCEC/KR, GSC, ESRI)

19. EcoCyc

20. Gene Ontology http://www.geneontology.org • “a dynamic controlled vocabulary that can be applied to all eukaryotes” • Built by the community for the community. • Three organising principles: • Molecular function, Biological process, Cellular component • Isa and Part of taxonomy – but not good! • ~10,000 concepts • Lightweight ontology, Poor semantic rigour. Ok when small and used for annotation. Obstacle when large, evolving and used for mining.

21. Controlled vocabulary • AGROVOC: Agricultural Vocabulary

22. Thesauri • AAT: Art & Architecture Thesaurus

23. Ontologies - Some Examples • General purpose ontologies: • WordNet / EuroWordNet, http://www.cogsci.princeton.edu/~wn • The Upper Cyc Ontology, http://www.cyc.com/cyc-2-1/index.html • IEEE Standard Upper Ontology, http://suo.ieee.org/ • Domain and application-specific ontologies: • RDF Site Summary RSS, http://groups.yahoo.com/group/rss-dev/files/schema.rdf • UMLS, http://www.nlm.nih.gov/research/umls/ • KA2 / Science Ontology, http://ontobroker.semanticweb.org/ontos/ka2.html • RETSINA Calendering Agent, http://ilrt.org/discovery/2001/06/schemas/ical-full/hybrid.rdf • AIFB Web Page Ontology, http://ontobroker.semanticweb.org/ontos/aifb.html • Web-KB Ontology, http://www-2.cs.cmu.edu/afs/cs.cmu.edu/project/theo-11/www/wwkb/ • Dublin Core, http://dublincore.org/ • Meta-Ontologies • Semantic Translation, http://www.ecimf.org/contrib/onto/ST/index.html • RDFT, http://www.cs.vu.nl/~borys/RDFT/0.27/RDFT.rdfs • Evolution Ontology, http://kaon.semanticweb.org/examples/Evolution.rdfs • Ontologies in a wider sense • Agrovoc, http://www.fao.org/agrovoc/ • Art and Architecture, http://www.getty.edu/research/tools/vocabulary/aat/ • UNSPSC, http://eccma.org/unspsc/ • DTD standardizations, e.g. HR-XML, http://www.hr-xml.org/

24. Concept “Jaguar“ Ontology Representation What is a „representation“? Represent

25. Ontology Representation Languages • Machines need communication with formal content to restrict meaning • What makes a language „formal“? • model theory (1st order predicate logic) • proof theory (Gentzen calculus) But also: • conventions (e.g. Java) Represent

26. For machine communication  model theory   proof theory  tracktability  strong conventions of use  human readable names  For human communication  strong conventions of use  human readable names   „natural“ primitives  What makes a language suitable?

27. Representation Paradigms (incomplete) TopicMaps Thesauri Taxonomies Ontologies Semantic Nets Represent extended ER-Modell Predicate Logics /Description Logics

28. Thesaurus Thesauri

29. Thesauri similarTo Vegetable Fruit Example: NarrowerTerm Orange Apfelsine (german) synonymWith • Graph with labels edges (similar, nt, bt, synonym) • Fixed set of edge labels (aka relations) • no instances Thesaurus • Well known in library science • cf. terminologies / classifications (Dewey)

30. Topic Maps

31. Topic Maps are ... • Standardized: ISO/IEC 13250:2000 • ISO standard published Jan. 2000 • enabling standard to describe knowledge structures,electronic indices, classification schemes, ... • Web enabled: • XML Topic Maps (XTM) are ready to use • Designed to: • manage the info glut • build valuable information networks above any kind of resources / data objects • enable the structuring of unstructured information

32. Back-of-the-Book Index “British Virgin Islands” Gorda Sound see North Sound Little Dix Bay .................... 89 North Sound ....................... 90 Road Harbour see also Road Town ... 73 Road Town ...................... 69,71Spanish Town ................... 81,82 Tortola ........................... 67Virgin Gorda ...................... 77

33. Back-of-the-Book Index “British Virgin Islands” Gorda Sound see North Sound Little Dix Bay .................... 89 North Sound ....................... 90 Road Harbour see also Road Town ... 73 Road Town ...................... 69,71Spanish Town ................... 81,82 Tortola ........................... 67Virgin Gorda ...................... 77 Topics

34. Back-of-the-Book Index “British Virgin Islands” Gorda Sound see North Sound Little Dix Bay .................... 89 North Sound ....................... 90 Road Harbour see also Road Town ... 73 Road Town ...................... 69,71Spanish Town ................... 81,82 Tortola ........................... 67Virgin Gorda ...................... 77 Occurrences

35. Back-of-the-Book Index “British Virgin Islands” Gorda Sound see North Sound Little Dix Bay .................... 89 North Sound ....................... 90 Road Harbour see also Road Town ... 73 Road Town ...................... 69,71Spanish Town ................... 81,82 Tortola ........................... 67Virgin Gorda ...................... 77 Different topic classes

36. Back-of-the-Book Index “British Virgin Islands” Gorda Sound see North Sound Little Dix Bay .................... 89 North Sound ....................... 90 Road Harbour see also Road Town ... 73 Road Town ...................... 69,71Spanish Town ................... 81,82 Tortola ........................... 67Virgin Gorda ...................... 77 Different occurrences classes

37. Back-of-the-Book Index “British Virgin Islands” Gorda Sound see North Sound Little Dix Bay .................... 89 North Sound ....................... 90 Road Harbour see also Road Town ... 73 Road Town ...................... 69,71Spanish Town ................... 81,82 Tortola ........................... 67Virgin Gorda ...................... 77 Multiple topic names

38. Back-of-the-Book Index “British Virgin Islands” Gorda Sound see North Sound Little Dix Bay .................... 89 North Sound ....................... 90 Road Harbour see also Road Town ... 73 Road Town ...................... 69,71Spanish Town ................... 81,82 Tortola ........................... 67Virgin Gorda ...................... 77 Association

39. Bay Island Town North Sound Little Dix Bay Tortola Spanish Town Road Town Virgin Gorda Road Harbour Topics – Computerized Subjects Topic classes Topics Subject Subject Subject Subject Subject Subject Subject Resources BVI Welcome SurfBVI CaribNet

40. North Sound Little Dix Bay Tortola Spanish Town Road Town Map Map Map Map Map Virgin Gorda Road Harbour Article Article Article Article Article Article Article Image Image Image Image Image BVI Welcome SurfBVI CaribNet Occurrences Topics Occurrences Occurrenceclasses Resources

41. North Sound Little Dix Bay Tortola Spanish Town Road Town Map Virgin Gorda Road Harbour Article Image Occurrences Topics Occurrences Occurrenceclasses Resources BVI Welcome SurfBVI CaribNet

42. Part-Whole Part-Whole Part-Whole Geo Containment Geo Containment Geo Containment Geo Containment North Sound Little Dix Bay Tortola Spanish Town Road Town Virgin Gorda Road Harbour Vicinity Vicinity Associations Association classes Associations Topics

43. Part-Whole Geo Containment North Sound Little Dix Bay Tortola Spanish Town Road Town Virgin Gorda Road Harbour Vicinity Associations Association classes Associations Topics

44. North Sound Little Dix Bay Tortola Spanish Town Road Town Virgin Gorda Bay Island Town Road Harbour Class Hierarchies Topic classes Topics

45. Land Bay North Sound Little Dix Bay Tortola Spanish Town Road Town Anchorbay Virgin Gorda Bay forswimming Suburb Capital Island Town Road Harbour Class Hierarchies Super-classes Sub-classes Topics

46. Political DependencyPolitische Abhängigkeit Geo ContainmentGeo Umschließung Brit. Virgin IslandsBrit. Jungferninseln Great BritainGroßbritannien CaribbeanKaribik ArticleArtikel MapKarte ImageBild BVI Welcome SurfBVI CaribNet Scopes

47. Political DependencyPolitische Abhängigkeit Geo ContainmentGeo Umschließung Brit. Virgin IslandsBrit. Jungferninseln Great BritainGroßbritannien CaribbeanKaribik ArticleArtikel MapKarte ImageBild BVI Welcome SurfBVI CaribNet Scopes Scopes

48. Political DependencyPolitische Abhängigkeit Geo ContainmentGeo Umschließung Brit. Virgin IslandsBrit. Jungferninseln Great BritainGroßbritannien CaribbeanKaribik ArticleArtikel MapKarte ImageBild Scopes Scopes Names: EnglishDeutsch BVI Welcome SurfBVI CaribNet

49. Political DependencyPolitische Abhängigkeit Geo ContainmentGeo Umschließung Brit. Virgin IslandsBrit. Jungferninseln Great BritainGroßbritannien CaribbeanKaribik ArticleArtikel MapKarte ImageBild Scopes Scopes Names: EnglishDeutsch Occurrences: PublicConfidential BVI Welcome SurfBVI CaribNet

50. Political DependencyPolitische Abhängigkeit Geo ContainmentGeo Umschließung Brit. Virgin IslandsBrit. Jungferninseln Great BritainGroßbritannien CaribbeanKaribik ArticleArtikel MapKarte ImageBild Scopes Scopes Associations: GeographyPolitics Names: EnglishDeutsch Occurrences: PublicConfidential BVI Welcome SurfBVI CaribNet