1. How can Computer Science contribute to Research Publishing?

2. Introduction to KRR Group

3. Who are we? Academics –Ian Horrocks –A. N. Other Research Students –Héctor Pérez-Urbina –Rob Shearer Research Staff –Bernardo Cuenca Grau –Birte Glimm –Yevgeny Kazakov –Boris Motik –Rob Shearer

4. What Do We Do? Knowledge representation (obviously) Ontologies and ontology languages Description logics –Formal underpinnings of ontology languages Reasoning problems and algorithms Implementation and optimisation of reasoning systems

5. Relevance to Publishing?

6. Annotations Key role will be played by annotations

7. Annotations Key role will be played by annotations But how can meaning be understood by software? Now... that should clear up a few things around here

8. Annotation Semantics Agree on meaning of a set of terms E.g., Dublin Core –Limited flexibility and extensibility –Limited number of things can be expressed Agree on language used to define meanings E.g., an ontology language –Flexible and extensible –New terms can be formed by combining existing ones –Meaning (semantics) of such terms is formally specified

9. What is an Ontology? A model of (some aspect of) the world Introduces vocabulary relevant to domain –Often includes names for classes and relationships Specifies intended meaning of vocabulary –Typically formalised using a suitable logic Closely related to schemas in the DB world –Instantiated by set of individuals and relations –Defines constraints on possible instantiations

11. Supporting 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

12. Supporting 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

13. Supporting 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

14. Range of new “non-standard” services supporting, e.g.: –Error diagnosis and repair Supporting Ontology Engineering

15. Range of new “non-standard” services supporting, e.g.: –Error diagnosis and repair –Modular design and integration What is the effect of merging O 2 into O 1 ? –Module Extraction Extract a (small) module from O capturing all “relevant” information about some vocabulary V –Bottom-up design Find a (small and specific) concept describing a set of individuals Supporting Ontology Engineering

16. In an Ontology based Information System (OIS), 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) OIS with no reasoner ¼ DBMS with no query engine Supporting Query Answering

17. Information Integration Ontologies provide unifying schema –Bridging between different data sources Query answering w.r.t.ontology –Date retrieved from relevant sources Similar to data integration in DBs –More flexible –Deductive capabilities

18. Driving User Interfaces Interface reflects structure of knowledge Query by navigation Semantically meaningful presentation of data –Easier understanding Context aware

19. Research Themes

20. Ontology Languages Standards crucial –Interoperability –Tool support W3C OWL ontology language standard –Central role in development of OWL language –Leading development of OWL 2 Extension to OWL driven by application requirements OWL 3? –Graphs –Integrity constraints –…–…

21. Scalability Integration of DLs with DBs Tractable ontology languages –Lightweight languages for data-intensive applications –Reasoning can be reduced to SQL querying New HermiT DL reasoner –Implements optimised hypertableau algorithm –Already outperforms existing reasoners –Aim is to push the limits of “practical” reasoning

22. New Reasoning Services Integration & extraction of modules Algorithms and practical techniques Incremental reasoning Methodologies and tool support

23. New Reasoning Services Conjunctive query answering Views –Definition and application in ontologies –Algorithms and tool support Information hiding and privacy –Lift/transform ideas from DB research –Reformulate as reasoning problems

24. Thank you for listening Any questions?