1. Web Explanations for Semantic Heterogeneity Discovery Pavel Shvaiko 2 nd European Semantic Web Conference (ESWC), 1 June 2005, Crete, Greece work in collaboration with Fausto Giunchiglia, Paulo Pinheiro da Silva and Deborah L. McGuinness

2. ESWC, June 1, 2005, Crete, Greece 2 Outline Introduction Semantic Matching Inference Web (IW) Framework Explaining Semantic Matching using IW Experimental Study Conclusions

3. ESWC, June 1, 2005, Crete, Greece 3 Introduction Information sources (e.g., database schemas, classifications or ontologies) can be viewed as graph-like structures containing terms and their inter-relationships Matching is one of the key operations for enabling the Semantic Web since it takes two graph-like structures and produces a mapping between the nodes of the graphs that correspond semantically to each other Matching, however, requires explanations because mappings between terms are not always intuitively obvious to human users

4. ESWC, June 1, 2005, Crete, Greece 4 Semantic Matching

5. ESWC, June 1, 2005, Crete, Greece 5 Semantic Matching Semantic Matching: Given two graphs G1 and G2, for any node n 1 i  G1, find the strongest semantic relation R’ holding with node n 2 j  G2 Computed R’s, listed in the decreasing binding strength order: equivalence { = }; more general/specific {, }; disjointness {  } We compute semantic relations by analyzing the meaning (concepts, not labels) which is codified in the elements and the structures of schemas/classifications Technically, labels at nodes written in natural language are translated into propositional logical formulas which explicitly codify the label’s intended meaning. This allows us to codify the matching problem into a propositional validity problem

6. ESWC, June 1, 2005, Crete, Greece 6 Example: Two simple classifications ? = Cyberspace and Virtual Reality Italy Europe Pictures Images Europe Italy Trento Computers and Internet D.E. A1 A2 Axioms  rel (Context 1, Context 2 ) (Images 1  Pictures 2 )  (Europe 1  Europe 2 )  (Images 1  Europe 1 )  (Europe 2  Pictures 2 ) Axioms Context 1 Context 2

7. ESWC, June 1, 2005, Crete, Greece 7 S-Match Expl.

8. ESWC, June 1, 2005, Crete, Greece 8 Inference Web (IW) Framework

9. ESWC, June 1, 2005, Crete, Greece 9 The IW Framework Overview Inference Web is a framework enabling applications to generate portable and distributed explanations for their answers

10. ESWC, June 1, 2005, Crete, Greece 10 Explaining Semantic Matching using IW

11. ESWC, June 1, 2005, Crete, Greece 11 Producing Explanations In order to explain mappings produced by S-Match and thereby increase the trust level of its users, we need to provide information about: background theories (e.g., WordNet) JSAT manipulations of propositional formulas WordNet

12. ESWC, June 1, 2005, Crete, Greece 12 Default Explanation A default explanation of mappings the S-Match system produces is a short, natural language, high-level explanation without any technical details. It is designed to be intuitive and understandable by ordinary users Query: find "European pictures" Query

13. ESWC, June 1, 2005, Crete, Greece 13 Explaining Background Knowledge Suppose that the agent still does not trust the answer and may want to see the sources of metadata information behind the mapping

14. ESWC, June 1, 2005, Crete, Greece 14 Explaining Logical Reasoning If the mappings derivation process needs to be explained, using the JSAT SAT engine, S-Match produces a trace of the DPLL procedure

15. ESWC, June 1, 2005, Crete, Greece 15 Experimental Study

16. ESWC, June 1, 2005, Crete, Greece 16 Preliminary Results Goal: to obtain a vision of how the S-Match explanations potentially scale to requirements of the Semantic Web

17. ESWC, June 1, 2005, Crete, Greece 17 Conclusions We use the Proof Mark-up Language for representing S-Match proofs, thus facilitating interoperability We use meaningful terms rather than numbers in the DIMACS format, thus facilitating understandability We use the IW tools, thus facilitating customizable, interactive proof and explanation presentation and abstraction Our solution is potentially scalable to the Semantic Web requirements

18. ESWC, June 1, 2005, Crete, Greece 18 Future Work Developing an environment, which efficiently exploits the IW proofs and explanations, in order to make the S-Match matching process (fully-fledged) interactive and iterative Improving the S-Match proofs and explanations by using abstraction techniques more extensively Conducting a user satisfaction study of the explanations Extending explanations to other SAT engines as well as to other non-SAT DPLL-based inference engines

19. ESWC, June 1, 2005, Crete, Greece 19 References Project website at DIT - ACCORD: http://www.dit.unitn.it/~accord/http://www.dit.unitn.it/~accord/ Project website at KSL - IW: http://iw.stanford.edu/http://iw.stanford.edu/ F. Giunchiglia, P. Shvaiko: Semantic matching. The Knowledge Engineering Review Journal, 18(3):265-280, 2003. F. Giunchiglia, P. Shvaiko, M. Yatskevich: S-Match: an algorithm and an implementation of semantic matching. In Proceedings of ESWS, pages 61-75, 2004. D. McGuinness, P. Pinheiro da Silva: Explaining Answers from the Semantic Web: The Inference Web Approach. Journal of Web Semantics, 1(4): 397- 413, 2004.

20. ESWC, June 1, 2005, Crete, Greece 20 Thank you!