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Knowledge Representation Issues for the Semantic Web

Knowledge Representation Issues for the Semantic Web. Jeff Heflin Lehigh University. Outline. Introduction History OWL Overview Selected Research Issues Semantics of Distributed Ontologies Reasoning and Scalability Overview of Other Key Research Topics. The Semantic Web. Definition

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Knowledge Representation Issues for the Semantic Web

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  1. Knowledge RepresentationIssues for the Semantic Web Jeff Heflin Lehigh University

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

  3. The Semantic Web • 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) • 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 • The Web is distributed • many sources, varying authority • inconsistency • The Web is dynamic • representational needs may change • The Web is enormous • systems must scale well • The Web is an open-world

  5. Evolution of Web Standards presentation-oriented markup HTML <tr><td><b>Charlotte’s Web</b> - E.B. White, Garth Williams. <font color=“Red”>$6.99</font> </td></tr> content-oriented markup XML <book> <title>Charlotte’s Web</title> <author>E.B. White</author> <author>Garth Williams</author> <price units=“USD”>6.99</price> <subject>Children’s Fiction</subject> </book>

  6. Web Ontology Language W3C Recommendation released Feb. 2004 OWL markup linked to semantics <rdf:Description rdf:about=“”> <imports resource=“www.books.com/bookont”> <rdf:Description> <Book rdf:ID=“book26489”> <author>E.B. White</author> <title>Charlotte’s Web</title> <price>6.99</price> <subject rdf:resource=“&bookont;FictionChild”> </Book> semantic markup <Class ID=“Book”> <Property ID=“subject”> <domain resource=“#Book”> <range resource=“#Topic”> </Property> <Class ID=“FictionChild”> <subclassOf resource=“#Fiction”> <subclassOf resource=“#Childrens”> </Class> … imports bookont ontology

  7. Ontology • 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 • Relevance to Web: • identify context • provide shared definitions • eases the integration of distinct resources

  8. Semantic Web Timeline May 2001 – Berners-Lee et al. Scientific American article Mar. 1996 - SHOE 0.90 (simple frames in HTML) Feb. 1998 – XML (semi-structured data for Web) Feb. 1999 – RDF (semantic nets in XML) Feb. 2004 – OWL (W3C Rec.) 1996 1998 2000 2002 2004 Jan. 1998 – SHOE 1.0 (frames + Horn logic) Sep. 1998 – Berners-Lee’s Semantic Web Roadmap Mar. 2001 – DAML+OIL (expressive DL in RDF) June. 2002 – 1st Int’l Semantic Web Conference

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

  10. URIs and Namespaces • 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 • Namespace • a mechanism for abbreviating URIs • by assigning a prefix for a URI fragment

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

  12. OWL Class Constructors borrowed from Ian Horrocks

  13. OWL RDF Syntax <owl:Class rdf:ID=”Band”> <rdfs:subClassOf> <owl:Restriction> <owl:onProperty rdf:resource=”#hasMember” /> <owl:allValuesFrom resource=”#Musician” /> </owl:Restriction> </rdfs:subClassOf></owl:Class> 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 • The head of an organization is also a member of it • A member of a terror organization is a terrorist • Therefore, the head of a terror organization is a terrorist <owl:Property rdf:ID=“head”> <rdf:subPropertyOf rdfs:resource=“member” /></owl:Property> <owl:Class rdf:ID=“Terrorist”> <owl:sameClassAs> <owl:Restriction> <owl:onProperty rdf:resource=“member” /> <owl:someValuesFrom rdf:resource=“TerroristOrg” /> </owl:Restriction> </owl:sameClassAs></owl:Class> type Bin Laden Terrorist head type Al Qaeda TerrorOrg

  16. Benefit of Description Logic • optimized computation of subsumption • calculate implicit subClassOf relations • 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 • OWL Full • very expressive (e.g., classes as instances) • theoretical properties not well understood • OWL DL • has a standard model theoretic semantics • OWL Lite • subset of OWL DL • easier to reason with

  18. Formal Semantics • 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 • OWL Full • non-standard RDF-style semantics • but still model-theoretic in nature

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

  20. A Web of Ontologies revises commits to A1 A2 S1 extends extends extends extends revises revises C1 B1 B2 B3 D1 extends extends extends commits to commits to commits to S4 E1 F1 S5 commits to commits to S2 S3

  21. Semantics of Ontology “Links” • 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. • DLs were one solution to this problem • In Semantic Web, links between ontologies now suffer from a similar lack of clear semantics

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

  23. Ontology Versioning • 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 • Issue: Should veh76 be a v2:Vehicle? http://ex.org/ont-v1 http://ex.org/ont-v2 type veh76 Vehicle Vehicle subClassOf type car54 Car

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

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

  26. OWL Versioning Syntax <rdf:rdf xmlns:owl="http://www.w3.org/2002/07/owl#" xmlns:rdf=“http://www.w3.org/1999/02/22-rdf-syntax-ns#” xmlns:rdfs=“http://www.w3.org/2000/01/rdf-schema#”><owl:Ontology rdf:about=“”> <owl:priorVersion rdf:resource=“http://ex.org/schema-v1”> <owl:backwardCompatibleWith rdf:resource=“http://ex.org/schema-v1”></owl:Ontology><owl:DeprecatedClass rdf:ID=“Megalodon”> <owl:Class rdf:ID=“Dolphin”> <rdfs:subClassOf rdf:resource=“#Mammal”></owl:Class></rdf:rdf> …

  27. Formal Ontology Definition • Ontology O=<V,A,E,P,B> • 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 • R is the set of resources • Knowledge function • maps resources to sets of wffs • K : R2W • Commitment function • maps resources to ontologies • C : R O

  29. Ontology Perspectives • Users may wish to view data through viewpoint of different ontologies • versioning is a special case of this • An ontology specifies a set of axioms • 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={O1,O2,…,On} where Oi=<Vi,Ai,Ei,Pi,Bi> axioms of basis ontology axioms of extended ontologies data from sources that commit to basis ontology or its ancestors 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: O1: A1 = {Dolphin(x) ® Fish(x)} B1 = {} O2: A2 = {Dolphin(x) ® Mammal(x)} B2 = {O1} Data: C(r1) = O1 K(r1) ={Dolphin(flipper), Fish(charlie), Mammal(bigfoot)} C(r2) = O2 K(r2) = {Dolphin(splasher)} Perspective Query 1 2 Dolphin(x) flipper flipper, splasher Fish(x) charlie, flipper charlie Mammal(x) bigfoot bigfoot, flipper, splasher

  32. Scalable Systems • Motivation • the Web is large • it won’t fit in main memory! • current systems don’t scale • 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. Use views to store class hierarchy Design – RDF(S) Entailment <owl:Class rdf:ID=”Student”/> <owl:Class rdf:ID="UndergraduateStudent"> <rdfs:subClassOf rdf:resource="#Student" /> <owl:Class/> CREATE VIEW Student_v AS SELECT * FROM Student UNION SELECT * FROM UndergraduateStudent_view

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

  35. Implementation – Query Query Interface application (Type GraduateStudent ?X) (TakeCourse ?X http://www.foo.edu/department0/course0) KIF-like conjunctive query Query API 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/course0 Query Translation Algorithm SQL Sentences RDBMS

  36. Lehigh University Benchmark • Can be used to evaluate semantic web reasoning systems • 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 • Metrics • load time • repository size • query response time • degree of completeness • degree of soundness

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

  38. Initial Experiment • Four systems tested • Sesame Memory, Sesame DB, OWLJessKB, DLDB • Five data sizes • ranging from 15 files (8 MB) to 999 files (583 MB) • 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 • Knowledge acquisition • Language design • Semantic Web services

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

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

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

  43. Conclusion • The Semantic Web is concerned with interoperability of distributed information • 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 • 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... • Useful websites • http://www.semwebcentral.org/ • http://www.w3.org/2001/sw/ • http://www.daml.org/ • http://www.semanticweb.org/ • My information • heflin@cse.lehigh.edu • http://www.cse.lehigh.edu/~heflin/

  45. The End

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

  47. Resolving Ontology Divergence Mapping Ontology Mapping Revisions Intersection Ontology O1 O2 O1 O2 O1 O2 ON OM O1¢ O2¢ O1¢ O2¢ OM contains rules that map concepts between the ontologies O1¢ contains rules that map O2 objects to O1 terminology. O2¢ does the reverse ON contains intersection of concepts. O1¢ and O2¢ rename terms where necessary revised by extended by Key:

  48. Ontologies_Index URL SeqNum http://www.lehigh.edu/~zhp2/univ-bench.owl 1 Source_Index URL SeqNum http://www.lehigh.edu/~zhp2/univ-bench.owl 1 file:/D:/demo/UBArtiData/University0_0.owl 2 URI_Index Implementation -Database Schema Student_1_view ID Source 1 1 3 1 TakeCourse_1 Subject Object Source 3 2 1 … … 1 URI ID http://www.Department0.University0.edu/UndergraduateStudent121 1 http://www.Department0.University0.edu/GraduateCourse9 2 http://www.Department0.University0.edu/GraduateStudent123 3

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