OIL: A Slick Way to Represent Knowledge on the Web

1. OIL: A Slick way to represent knowledge on the web Carole Goble, Ian Horrocks University of Manchester and The OIL Consortium

2. Describing and Exchanging Ontologies • To reuse an ontology we need to share it with others in the community • Exchanging ontologies requires a language with: • common syntax • clear and explicit shared meaning • Tools for parsing, delivery, visualising etc

3. XOL eXtensible Ontology Language • XML markup • Frame based • Based on OKBC-Lite • iXOL by KSL Stanford • http://www.ai.sri.com/pkarp/xol/

4. OIL: Ontology Inference Layer Frames: modelling primitives, OKBC-Lite Description Logics: formal semantics & Automated reasoning support OIL Web languages: XML & RDF based syntax • The language formally known as Ontology Interchange language • Extends XOL (some current restrictions) • Gives a semantics to RDF-Schema • http://www.ontoknowledge.org/oil A knowledge representation language and inference mechanism for the web

5. OIL: Ontology Metadata (Dublin Core) Ontology-container title “macromolecule fragment” creator “robert stevens” subject “macromolecule generic ontology” description “example for a tutorial” description.release “1.0” publisher “R Stevens” type “ontology” formal “pseudo-xml” identifier “http://www.ontoknowledge.org/oil/oil.pdf” source “http://img.cs.man.ac.uk/ismb00/mmexample.pdf” language “OIL” language “en-uk” relation.haspart “http://www.ontoRus.com/bio/mmole.onto”

6. OIL primitive ontology definitions slot-defhas-backbone inverseis-backbone-of slot-defpart-of inverse is -part-of properties transitive class-def rna subclass-ofnucleic-acid slot-constrainthas-backbone value-typeribophosphate class-def ribophosphate class-def deoxyribophosphate subclass-of NOT ribophosphate

7. OIL defined ontology definitions class-def defined dna subclass-of nucleic-acid slot-constraint has-backbone value-type deoxyribophosphate class-def dna subclass-of NOT rna class-def defined catalyst subclass-of macromolecule slot-constraint promotes has-value reaction class-def defined enzyme subclass-of protein, catalyst

8. OIL defined ontology definitions class-def definedmitochondrial subclass of location slot-constraint cellularlocation cardinality 1 ((has-value mitochondrion) OR (slot-constraint part-of has-value mitochondrion)) class-def defined succinate-dehydrogenase subclass of enzyme slot-constraint promotes value-type oxidation slot-constraint cellularlocation cardinality 1 (has-value (slot-constraint part-of has-value mitochondrion)

9. OIL in XML OIL has a DTD, an XML Schema and a mapping to RDF-Schema. See web site for details <slot-def> <slot-name = “has-component”/> <inverse> <slot-name = “is-component-of”/> </inverse> <properties> <transitive/> </properties> </slot-def> <class-def> <class-name= “nucleic-acid”/></class-def> <class-def> <class-name= “rna”/> <subclass-of> <class name = “nucleic-acid”/></subclass-of> <slot-constraint> <slot-name = “has-backbone”/> <value-type> <class name= “ribophosphate”</value-type> </slot-constraint> </class-def>

10. OIL Extensibility Model Rules/ Axioms Default Reasoning Decidable Core Instances in Class Definitions Limited 2nd order expressivity Concrete domains (integers, strings) are on their way to the core...

11. OIL Directions • Tools: • FaCT reasoner with CORBA IDL • OilEd: Protégé II + FaCT reasoner • Chimaera • OntoEdit • DARPA Agent Markup Language initiative • OIL will be the language likely to be adopted • W3C • Collaborators on DAML • OIL likely for Semantic Web language • Other projects: • Semantic Web projects (www.semanticweb.org) • OntoWeb Network of Excellence • SemanticWeb IST EU Proposal

12. Tim Berners-Lee’s Semantic Web “My definition of the Web is a universe of network-accessible information, • a means of human-to-human communication, and • a space in which software agents can, though access to a vast amount of everything which is society, science and its problems, become tools to work with us.” Web Architecture from 50,000 feet http://www.w3.org/DesignIssues/Architecture.html From Machine Readable to Machine understandable

13. The OIL Acknowledged Contributors • Ian Horrock and Carole Goble, Department of Computer Science,University of Manchester, UK • Dieter Fensel, Michel Klein, Frank van Harmelen, Ying Ding, Rainer Faulstich, Borys Omelayenko and Hans Akkermans, Vrije Universiteit Amsterdam, Nederland • Jeen Broekstra, Frank van Harmelen, Jos van der Meer and Christiaan Fluit, Aidministrator, Nederland • Guus Schreiber, SWI, University of Amsterdam, Nederland • Enrico Motta, Knowledge Media Institute, Open University, UK • Deborah McGuinness, Stefan Decker, Stanford University, USA • Michael Erdmann, Stefan Staab, Alexander Maedche and Rudi Studer, Institute AIFB, University of Karlsruhe, Germany • Peter Karp, SRI International's Artificial Intelligence Center, USA • Monika Crubezy, Stefan Decker, William Grosso, and Mark Musen, Knowledge Modeling Group at Stanford Medical Informatics, Stanford University, USA • Heiner Stuckenschmidt, University of Bremen, Germany • Robin McEntire, SmithKline Beecham • John Davis, John Hughes, Uwe Krohn, British Telecom • Ulrich Reimer, Martin Staudt, Swiss Life • Robert Engels, CognIT • Bernt A. Bremdal and Fredrik Ygge, EnerSearch