1. Opportunistic Reasoning for the Semantic Web: Adapting Reasoning to the Environment Carlos Pedrinaci Tim Smithers and Amaia Bernaras

2. The Semantic Web After 10 years research on the Semantic Web has already produced a considerable amount of technologies RDF OWL WSMO OWL-S Sesame Jena Pellet KAON Kowari YARS IRS WSMX Aqualog Flink eMerges DBin

3. So Far, So Good But… The main focus is essentially on the development of tools that generate data represented in Semantic Web languages Applications are mainly limited to query engines and information aggregation

4. So Far, So Good But… Where is the killer application? Which are the distinguishing features with respect to Web 2.0? Why are companies somehow reluctant to embracing the Semantic Web? Indeed, the difficulties are not just technical but we need to get these right!

5. Some Technical Issues Scalability –The Web is huge (and is growing) –Reasoning is expensive –Need for further expressivity which makes things worse –Knowledge Engineering is particularly challenging

6. Some Technical Issues Completeness –Not possible within such a dynamic environment –We need to reason in an opportunistic and incremental manner Consistency & Correctness –Not realistic for the Web –We need non-monotonic reasoning and truth-maintenance mechanisms

7. Some Technical Issues The Web is essentially dynamic –The previous problems just get worse Worst of all, a Semantic Web application should deal with all these issues, plus the typical Engineering challenges!

8. Lets Shift the Focus! Lets view the Semantic Web as a phenomenon emerging from the interaction of intelligent applications over the Web and not as an entity in itself In this scenario Scalability, Expressivity, Completeness, Consistency, Trustworthyness more manageable issues

9. Lets Shift the Focus! Lets focus on supporting the development of KBS for the Web Based on the technologies developed so far Reconciling Knowledge Engineering and Software Engineering best practices

10. Opportunistic Reasoning Ability of a system to exploit its best data and most promising methods Erman et al. 1988

11. Blackboard Model

12. Blackboard Characteristics Divide and conquer - Problem- solving expertise partitioning –Simplifies Knowledge Modelling –Promotes reuse –Applicability of diverse representation and reasoning techniques Collaborative and concurrent reasoning Event-based reasoning

13. Blackboard Applicability Criteria A large solution space Noisy or unreliable problem data A continuous data flow The need to integrate diverse and heterogeneous data The need to integrate different sources of knowledge

14. Blackboard Applicability Criteria The need to apply several reasoning methods The need to develop various lines of reasoning The need for incremental reasoning The need for an opportunistic control of the reasoning process

15. Blackboard Applicability Criteria The need for an event-based activation of the reasoning High complexity of the task The need for a mixed initiative where computer and users can interchangeably take the initiative Meta-reasoning or conscious reasoning –Drive & Explain the reasoning process

16. General Applicability

20. Applicability to the Web Particularly well-suited for the Web –Opportunistic Reasoning Adapted to the dynamism of the Web –Flexible and versatile Wide applicability, seamless integration of diverse reasoning engines, languages, and tools –Modular Maintainable, extensible –Distributable Computation distribution Adapted to the Web (Ontologies, URIs, Web Services)

21. An Infrastructure…

22. Several Applications… Web-based Events Design Support System Music Rights Clearing Organization

23. Conclusion Considering the Semantic Web as an emerging phenomenon has important pragmatic consequences Opportunistic Reasoning seems particularly appropriate for reasoning over the Web Appealing characteristics from an Engineering perspective that make it a good candidate for supporting Semantic Web applications

24. Thanks Thank you for your attention