2. Knowledge Representation Ontology are best delivered in some computable representation Variety of choices with different: –Expressiveness The range of constructs that can be used to formally, flexibly, explicitly and accurately describe the ontology –Ease of use –Computational complexity Is the language computable in real time –Rigour Satisfiability and consistency of the representation Systematic enforcement mechanisms –Unambiguous, clear and well defined semantics A subclassOf B don’t be fooled by syntax!

3. Languages Vocabularies using natural language –Hand crafted, flexible but difficult to evolve, maintain and keep consistent, with poor semantics –Gene Ontology Object-based KR: frames –Extensively used, good structuring, intuitive. Semantics defined by OKBC standard –EcoCyc (uses Ocelot) and RiboWeb (uses Ontolingua) Logic-based: Description Logics –Very expressive, model is a set of theories, well defined semantics –Automatic derived classification taxonomies –Concepts are defined and primitive –Expressivity vs. computational complexity balance –TAMBIS Ontology (uses FaCT)

4. Vocabularies: Gene Ontology Hand crafted with simple tree-like structures Position of each concept and its relationships wholly determined by a person Flexible but… Maintenance and consistency preservation difficult and arduous Poor semantics Single hierarchies are limiting

5. Frame Data Model Frames –Classes: Genes, Reactions –Instances: Relationships –Slots: Chromosome, map-position, citations, reactants, products, Keq –Facets: Chromosome is single-valued, instance of class Chromosomes; Citations is multiple valued, set of strings Ontolingua the most famous frame system All frames asserted into taxonomy by hand All concepts are primitive

6. Description Logics Describe knowledge in terms of concepts and relations Concept defined in terms of other roles and concepts –Enzyme = protein which catalyses reaction –Reason that enzyme is a kind of protein Model built up incrementally and descriptively Uses logical reasoning to figure out: –Automatically derived (and evolved) classifications –Consistency -- concept satisfaction

7. Frames and Logics Frames –Rich set of language constructs –Impose restrictive constraints on how they are combined or used to define a class –Only support primitive concepts –Taxonomy hand-crafted Description logics –Limited set of language constructs –Primitives combined to create defined concepts –Taxonomy for defined concepts established though logical reasoning –Expressivity vs. computational complexity –Less intuitive Ideal: both! Current activity uses a mixture. Logics provide reasoning services for frame schemes.

8. Ontology Exchange 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 Exchanging the structure, semantics or conceptualisation?

9. Ontology Exchange Languages XOL eXtensible Ontology Language –XML markup –Frame based –Rooted in OKBC –http://www.ai.sri.com/pkarp/xol/ OIL Ontology Interchange language  Ontology Inference Layer –Gives a semantics to RDF-Schema –http://www.ontoknowledge.org/oil Frames: modelling primitives, OKBC Description Logics: formal semantics & reasoning support Web languages: XML & RDF based syntax OIL

10. 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”

11. The Three Roots of OIL Frame-based Systems: Epistemological Modelling Primitives Web Languages: XML- and RDF-based syntax Description Logics: Formal Semantics & Reasoning Support OIL

12. OIL primitive ontology definitions slot-def has-backbone inverse is-backbone-of slot-def has-component inverse is -component-of properties transitive class-def nucleic-acid class-def rna subclass-of nucleic-acid slot-constraint has-backbone value-type ribophosphate class-def ribophosphate class-def deoxyribophosphate subclass-of NOT ribophosphate

13. OIL defined ontology definitions class-def defined dna subclass-of nucleic-acid AND NOT rna slot-constraint has-backbone value-type deoxyribophosphate class-def defined enzyme subclass-of protein slot-constraint catalyse has-value reaction class-def defined kinase subclass-of protein slot-constraint catalyse has-value phosphorylation-reaction

14. OIL in XML OIL has a DTD, an XML Schema and a mapping to RDF-Schema. See web site for details

15. OIL Remarks Tools: –Protégé II editor –FaCT reasoner Other projects: –Semantic Web projects (www.semanticweb.org) –Agents for the web projects (e.g. DAML) A knowledge representation language and inference mechanism for the web

16. OIL Features Based on standard frame languages Extends expressive power with DL style logical constructs –Still has frame look and feel –Can still function as a basic frame language OIL core language restricted in some respects so as to allow for reasoning support –No constructs with ill defined semantics –No constructs that compromise decidability Has both XML and RDF(S) based syntax

17. OIL Features Semantics clearly defined by mapping to very expressive Description Logic, e.g.: –slot-constraint eats has-value meat, fish –   eats.meat   eats.fish Note the importance of clear semantics: –  eats.(meat  fish) is inconsistent (assuming meat and fish are disjoint) Mapping can also be used to provide reasoning support from a Description Logic system (e.g., FaCT)

18. Why Reasoning Support? Key feature of OIL core language is availability of reasoning support Reasoning intended as design support tool –Check logical consistency of classes –Compute implicit class hierarchy May be less important in small local ontologies –Can still be useful tool for design and maintenance –Much more important with larger ontologies/multiple authors Valuable tool for integrating and sharing ontologies –Use definitions/axioms to establish inter-ontology relationships –Check for consistency and (unexpected) implied relationships –Already shown to be useful technique for DB schema integration