1. Knowledge Representation Issues for the Semantic Web Jeff Heflin Lehigh University

2. Outline u Introduction –History –OWL Overview u Selected Research Issues –Semantics of Distributed Ontologies –Reasoning and Scalability u Overview of Other Key Research Topics

3. The Semantic Web u Definition –The Semantic Web is not a separate Web but an extension of the current one, in which information is given well-defined meaning, better enabling computers and people to work in cooperation. (Berners-Lee et al., Scientific American, May 2001) u Applications –managing corporate web sites (intranets) –more automatic generation of web portals –better indexing of multimedia resources –web agents and web services –ubiquitous computing

4. Semantic Web Challenges u The Web is distributed –many sources, varying authority –inconsistency u The Web is dynamic –representational needs may change u The Web is enormous –systems must scale well u The Web is an open-world

5. Evolution of Web Standards XML Charlotte’s Web - E.B. White, Garth Williams. $6.99 Charlotte’s Web E.B. White Garth Williams 6.99 Children’s Fiction presentation-oriented markup content-oriented markup HTML

6. OWL u Web Ontology Language –W3C Recommendation –released Feb. 2004 E.B. White Charlotte’s Web 6.99 … imports bookont ontology markup linked to semantics semantic markup

7. Ontology u Definition –a logical theory that accounts for the intended meaning of a formal vocabulary (Guarino 98) –has a formal syntax and unambiguous semantics –inference algorithms can compute what logically follows u Relevance to Web: –identify context –provide shared definitions –eases the integration of distinct resources

8. Semantic Web Timeline Mar. 1996 - SHOE 0.90 (simple frames in HTML) Jan. 1998 – SHOE 1.0 (frames + Horn logic) Feb. 1998 – XML (semi- structured data for Web) 19962004200020021998 Sep. 1998 – Berners-Lee’s Semantic Web Roadmap Feb. 1999 – RDF (semantic nets in XML) Mar. 2001 – DAML+OIL (expressive DL in RDF) May 2001 – Berners-Lee et al. Scientific American article June. 2002 – 1 st Int’l Semantic Web Conference Feb. 2004 – OWL (W3C Rec.)

9. RDF and RDF Schema u:Chair John Smith rdf:type g:name John Smith g:Person g:name rdfs:Classrdfs:Property rdf:type rdfs:subclassOf rdfs:domain <rdfs:subclassOf rdf:resource= “http://schema.org/gen#Person”>

10. URIs and Namespaces u URI –Uniform Resource Identifier –includes URLs –but also anything that you can design an identification scheme for –helps to prevent collision of names –all the “symbols” in RDF are either URIs or Literals u Namespace –a mechanism for abbreviating URIs –by assigning a prefix for a URI fragment

11. OWL u RDF is a data language –OWL adds ontologies to RDF –used to define RDF classes and properties u OWL ontologies are written in RDF syntax u semantically, OWL is based on description logics –tradeoff between expressivity and computability

12. OWL Class Constructors borrowed from Ian Horrocks

13. OWL RDF Syntax A Band is a subset of the set of objects which only have Musicians as members

14. OWL Axioms borrowed from Ian Horrocks

15. OWL Inference Bin Laden Al QaedaTerrorOrg Terrorist type head type u The head of an organization is also a member of it u A member of a terror organization is a terrorist u Therefore, the head of a terror organization is a terrorist

16. Benefit of Description Logic u optimized computation of subsumption –calculate implicit subClassOf relations u ontology integration –if two ontologies use class expressions to define their vocabularies in terms of a third ontology, then subsumption can be used to compute an integrated ontology

17. Species of OWL u OWL Full –very expressive (e.g., classes as instances) –theoretical properties not well understood u OWL DL –has a standard model theoretic semantics u OWL Lite –subset of OWL DL –easier to reason with

18. Formal Semantics u OWL Lite and OWL DL –fairly standard DL-style model theoretic semantics –defined using interpretations –classes are sets of objects –class constructors and axioms place conditions on interpretations u OWL Full –non-standard RDF-style semantics –but still model-theoretic in nature

19. Selected Research Issues u Work by the SWAT lab at Lehigh –students »Yuanbo Guo »Zhengxiang Pan u Semantics for distributed ontologies u Reasoning and scalability

20. A Web of Ontologies A1A1 A2A2 B3B3 B1B1 B2B2 C1C1 D1D1 E1E1 F1F1 revises extends S1S1 S2S2 S3S3 S5S5 S4S4 commits to

21. Semantics of Ontology “Links” u Brachman (1983) regarding links between concepts in early semantic networks –... the meaning of the link was often relegated to “what the code does with it”- neither an appropriate notion of semantics nor a useful guide for figuring out what the link, in fact means. u DLs were one solution to this problem u In Semantic Web, links between ontologies now suffer from a similar lack of clear semantics

22. owl:imports u ontology extension / commitment u semantics –in order to satisfy an ontology, an interpretation must also satisfy all ontologies that it imports u only provides semantics for each document in isolation!

23. Ontology Versioning u Each new version has new URL –other users may have committed to your ontology »“point at” it using its URL –if you change the file at that location, then you change their commitment without their consent u Issue: Should veh76 be a v2:Vehicle? car54 Vehicle type http://ex.org/ont-v2 Car subClassOf veh76 Vehicle type http://ex.org/ont-v1

24. Versioning Complications u Should Flipper be a v2:Mammal? –depends »is change to correct a modeling error? »or to reflect a change in interpretation of “Dolphin”? Dolphin Fish subClassOf http:/ex.org/schema-v2 http://ex.org/schem-v1 Mammal subClassOf Flipper type

25. Versioning in OWL u priorVersion –indicates a previous version of an ontology u backwardCompatibleWith –indicates a version with which ontology is backward compatible u DeprecatedClass –used to signify that a class should no longer be used u DeprecatedProperty –used to signify that a property should no longer be used u versionInfo –used for CVS-like strings u incompatibleWith –opposite of backwardCompatible with

26. OWL Versioning Syntax …

27. Formal Ontology Definition u Ontology O= –V = vocabulary (a set of symbols) –A = axioms (a set of wffs) –E = set of extended ontologies –P = set of prior versions of ontology –B = set of ontologies O is backward-compatible with (subset of P)

28. Resource Definitions u R is the set of resources u Knowledge function –maps resources to sets of wffs –K : R  2 W u Commitment function –maps resources to ontologies –C : R  O

29. Ontology Perspectives u Users may wish to view data through viewpoint of different ontologies –versioning is a special case of this u An ontology specifies a set of axioms u Ontology perspectives specify a logical theory based on an ontology and a set of data sources –combine axioms and ground atoms –queries are with respect to a perspective

30. Ontology Perspective Theory Given O={O 1,O 2,…,O n } where O i = axioms of basis ontology data from sources that commit to basis ontology or its ancestors axioms of extended ontologies data from sources that commit to ontologies that are compatible with the basis ontology data from sources that commit to ontologies that are compatible with ancestors of the basis ontology

31. Perspectives Example Ontologies: O 1 : A 1 = {Dolphin(x)  Fish(x)} B 1 = {} O 2 : A 2 = {Dolphin(x)  Mammal(x)} B 2 = {O 1 } Data: C(r 1 ) = O 1 K(r 1 ) ={Dolphin(flipper), Fish(charlie), Mammal(bigfoot)} C(r 2 ) = O 2 K(r 2 ) = {Dolphin(splasher)} Perspective Query 11 22 Dolphin(x) Fish(x) Mammal(x) flipper charlie, flipper bigfoot flipper, splasher charlie bigfoot, flipper, splasher

32. Scalable Systems u Motivation –the Web is large »it won’t fit in main memory! –current systems don’t scale u DLDB –DB: Relational Database (Microsoft ® Access) »scalable technology for querying data –DL: Description Logics (FaCT reasoner) »rich inference capability »close correspondence to semantics of OWL

33. Design – RDF(S) Entailment u Use views to store class hierarchy CREATE VIEW Student_v AS SELECT * FROM Student UNION SELECT * FROM UndergraduateStudent_view

34. Design – OWL Entailment Inferred Hierarchy DL Reasoner Ontology table & view creation Database operation … Student  Person who takes a Course GraduateStudent  Person who takes a GraduateCourse GraduateCourse  Course … Graduate Student  Student … CREATE VIEW Student_1_view AS SELECT * FROM Student_1 UNION SELECT * FROM UndergraduateStudent_1_view UNION SELECT * FROM GraduateStudent_1_view;

35. Implementation – Query (Type GraduateStudent ?X) (TakeCourse ?X http://www.foo.edu/department0/co urse0) SELECT GraduateStudent_2_view.ID FROM GraduateStudent_2_view, takeCourse_2_view WHERE GraduateStudent_2_view.id = takeCourse_2_view.subject AND takeCourse_2_view.object= http://www.foo.edu/department0/cour se0 Query Interface application KIF-like conjunctive query Query Translation Algorithm SQL Sentences RDBMS Query API

36. Lehigh University Benchmark u Can be used to evaluate semantic web reasoning systems u Features –OWL ontology for university domain (moderate complexity) –customizable data generation »can select number of universities and random number generator seed »arbitrary size »repeatable –plausible »“real world” constraints are applied u Metrics –load time –repository size –query response time –degree of completeness –degree of soundness

37. Benchmark System Repository 1 Repository N API Benchmark Data Data Generator 14 Test Queries* Tester Univ-Bench Ontology Test Results *each query is executed by 10 times to account for caching.

38. Initial Experiment u Four systems tested –Sesame Memory, Sesame DB, OWLJessKB, DLDB u Five data sizes –ranging from 15 files (8 MB) to 999 files (583 MB) u Summary of results –Sesame-Memory best for small to medium size if only RDFS inference is needed –OWLJessKB can answer queries none of the other systems can »but doesn’t scale and makes some unsound inferences –DLDB has best balance between query response time and completeness

39. Some Other Research Topics u Knowledge acquisition u Language design u Semantic Web services

40. Knowledge Acquisition u data –create or find relevant ontology –then either »convert existing forms to RDF u e.g., XML, relational DBs, CGs, etc. »information extraction »natural language processing »controlled English? (Sowa, yesterday) u ontologies –import existing ontologies –manual creation (e.g., Protogé) –machine learning –formal concept analysis? (Rudolph, yesterday)

41. Language Design u DL is insufficient for some applications u Significant demand for “rules” –Combining logic programming with DL (Grosof et al. 2003) u SWRL (Semantic Web Rule Language) –proposal to add Horn logic to OWL u However, must consider expressivity / scalability tradeoff

42. Semantic Web Services u Web service –a web-accessible program that provides information or performs an action u OWL-S –ontology for describing web services »consists of profile, process model, and grounding u Current research includes: –matchmaking (e.g., see work of Sycara) –automated composition (e.g., see work of McIlraith) –much more …

43. Conclusion u The Semantic Web is concerned with interoperability of distributed information u OWL is a standard that allows for sharing of ontologies –if you want your ontologies to be used by the world, then export (what you can) to OWL u There is much research to do before the Semantic Web problem is solved –we need all the help we can get!

44. For more information... u Useful websites –http://www.semwebcentral.org/ –http://www.w3.org/2001/sw/ –http://www.daml.org/ –http://www.semanticweb.org/ u My information –heflin@cse.lehigh.edu –http://www.cse.lehigh.edu/~heflin/

45. The End

46. Ontology Divergence u The Web is distributed and dynamic u Therefore, ontological differences will arise –terminology –scope –encoding –context Thing Car Civic Automobile Delorean Object general-ontology trans-ontvehicle-ont isa PorscheEscort

47. Resolving Ontology Divergence O1O1 O2O2 O1O1 O2O2 O1O1 O2O2 O1O1 O2O2 O1O1 O2O2 OMOM ONON Mapping OntologyMapping RevisionsIntersection Ontology O M contains rules that map concepts between the ontologies O 1 contains rules that map O 2 objects to O 1 terminology. O 2 does the reverse O N contains intersection of concepts. O 1 and O 2 rename terms where necessary revised by extended by Key:

48. Implementation - Database Schema Student_1_view 1http://www.lehigh.edu/~zhp2/univ-bench.owl SeqNumURL Ontologies_Index 2file:/D:/demo/UBArtiData/University0_0.owl 1http://www.lehigh.edu/~zhp2/univ-bench.owl SeqNumURL Source_Index 2 http://www.Department0.University0.edu/GraduateCourse9 3 http://www.Department0.University0.edu/GraduateStudent123 1 http://www.Department0.University0.edu/UndergraduateStudent121 IDURI 13 11 SourceID TakeCourse_1 … 2 Object 1… 13 SourceSubject URI_Index