1. Semantic Web The Story So Far Ian Horrocks Oxford University Computing Laboratory

2. Semantic Web

3. According to W3C “an evolving extension of the World Wide Web in which web content can be … read and used by software agents, thus permitting them to find, share and integrate information more easily” Data will use uniform syntactic structure (RDF) (OWL) ontologies will provide –Schemas for data –Vocabulary for annotations Ultimate goal is a “more intelligent web” Semantic Web

4. Semantic Web led to requirement for a “web ontology language” set up Web-Ontology (WebOnt) Working Group –WebOnt developed OWL language –OWL based on earlier languages RDF, OIL and DAML+OIL –OWL now a W3C recommendation (i.e., a standard) OWL is a family of 3 languages: OWL Lite, OWL DL and OWL Full OIL, DAML+OIL and OWL (DL & Lite) based on Description Logics –Has facilitated development of wide range of high quality tools & infrastructure OWL now language of choice in many applications Web Ontology Language OWL

5. What Are Description Logics? A family of logic based Knowledge Representation formalisms –Descendants of semantic networks and KL-ONE –Describe domain in terms of concepts (AKA classes), roles (AKA properties, relationships) and individuals –Operators allow for composition of complex concepts –Names can be given to complex concepts, e.g.: HappyParent ´ Parent u 8 hasChild.(Intelligent t Athletic)

6. Why (Description) Logic? OWL exploits results of 15+ years of DL research –Well defined (model theoretic) semantics –Most DLs are subsets of C2, i.e., decidable fragments of FOL

7. Why (Description) Logic? OWL exploits results of 15+ years of DL research –Well defined (model theoretic) semantics –Formal properties well understood (complexity, decidability) [Garey & Johnson. Computers and Intractability: A Guide to the Theory of NP-Completeness. Freeman, 1979.] I can’t find an efficient algorithm, but neither can all these famous people.

8. Why (Description) Logic? OWL exploits results of 15+ years of DL research –Well defined (model theoretic) semantics –Formal properties well understood (complexity, decidability) –Known reasoning algorithms

9. Why (Description) Logic? OWL exploits results of 15+ years of DL research –Well defined (model theoretic) semantics –Formal properties well understood (complexity, decidability) –Known reasoning algorithms –Implemented systems (highly optimised) Pellet KAON2 CEL

10. Ontology Based Information Systems Similar to relational databases –Ontology ¼ schema; instances ¼ data Some important (dis)advantages +(Relatively) easy to maintain and update schema Schema plus data are integrated in a logical theory +Query answers reflect both schema and data +Able to answer both intensional and extensional queries –Semantics may be counter-intuitive or even inappropriate Open -v- closed world; axioms -v- constraints –Query answering (logical entailment) much more difficult Can lead to scalability problems

11. Ontology Based Information Systems Similar to relational databases –Ontology ¼ schema; instances ¼ data Some important (dis)advantages +(Relatively) easy to maintain and update schema Both schema and data are “self organising” +Query answers reflect both schema and data +Able to answer both intensional and extensional queries –Semantics may be counter-intuitive or even inappropriate Open -v- closed world; axioms -v- constraints –Query answering (logical entailment) much more difficult Can lead to scalability problems Useful, but not miraculous!

12. Ontologies and Reasoning

13. Support for Ontology Engineering Developing and maintaining quality ontolgies is very challenging Users need tools and services, e.g., to help check if ontology is: –Meaningful — all named classes can have instances

14. Support for Ontology Engineering Developing and maintaining quality ontolgies is very challenging Users need tools and services, e.g., to help check if ontology is: –Meaningful — all named classes can have instances –Correct — captures intuitions of domain experts

15. Support for Ontology Engineering Developing and maintaining quality ontolgies is very challenging Users need tools and services, e.g., to help check if ontology is: –Meaningful — all named classes can have instances –Correct — captures intuitions of domain experts –Minimally redundant — no unintended synonyms  Banana splitBanana sundae

16. Support for Query Answering In an Ontology Based Information System (OBIS), Query answering ¼ computing logical entailment –Reasoner needed in order to answer queries, e.g.: C is a sub-class of D iff O ² 8 x. C(x) ! D(x) a is an instance of C iff O ² C(a) OBIS with no reasoner ¼ DBMS with no query engine

17. Recent Developments

18. OWL 1.1 Is an extension of OWL –Addresses deficiencies identified by users and developers (at OWLED workshop) Is based on more expressive DL: SROIQ –(OWL is based on SHOIN ) W3C working group now chartered –Will develop recommendation based on existing member submission Already supported by popular OWL tools –Protégé, Swoop, TopBraid, FaCT++, Pellet

19. Tool Support for Modular Design Check when integration of modules is “safe” –Interface between modules via exported vocabulary –Information flows from imported to importing ontology –No information flows back the other way Extract smaller modules from large ontologies –E.g., starting with SNOMED, extract module for “Heart” –Tool should ensure that module Is small (and preferably minimal), but Still contains all “relevant knowledge” [Cuenca Grau & Kazakov, IJCAI-07 & WWW-07]

20. Extending Expressive Power Database style keys [Lutz et al, JAIR 2004] –E.g., make + model + chassis-number is a key for Vehicles Rule language extensions –W3C RIF WG (see http://www.w3.org/2005/rules/)http://www.w3.org/2005/rules/) –First order extensions (e.g., SWRL) [Horrocks et al, JWS, 2005] –Hybrid language extensions, e.g., [Eiter et al, KR-04; Motik et al, ISWC-04; Rosati, JoWS, 2005] –LP/F-Logic/Common Logic [Chen et al, JLP, 1993; de Bruijn et al, WWW-05] Other extensions –Temporal –Fuzzy –Extended annotation framework –Macro language –…–…

21. Extended Query Language Standard reasoning techniques only provide for simple queries –E.g., return all instances of a (possibly complex) concept C Practical applications may need a richer query language –E.g., retrieve tuples (?x, ?y, ?z), where: ?x is an R5 Phosphatase, ?x contains the phosphatase domains (p-domains) ?y and ?z, ?y is a Catalytic domain, and ?z is a Fibronectin domain

22. Improving Scalability Optimisation techniques –Improve performance of DL reasoners, e.g., [Tsarkov, Horrocks et al, JAR, 2007] New Reasoning Techniques –Reduction to disjunctive Datalog [Motik et at, KR-04] Transform SHOIN ontology into Datalog Ç program Use LP techniques to deal with large numbers of ground facts –Hybrid DL-DB systems [Horrocks et al, CADE-05] Use DB to store “Abox” (individual) axioms Cache inferences and use DB queries to answer/scope logical queries –Hypertableau based algorithms [Motik et al, CADE-07] Prototypical implementation in HermiT system Polynomial time algorithms for sub-ALC logics –Graph based techniques for EL+ [Baader et al, IJCAI-05] –Database techniques for DL-Lite [Calvanese et al, AAAI-05]

23. Thank you for listening

24. Any questions? FRAZZ: © Jeff Mallett/Dist. by United Feature Syndicate, Inc.