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Web Explanations for Semantic Heterogeneity Discovery Pavel Shvaiko 2 nd European Semantic Web...

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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
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Page 1: Web Explanations for Semantic Heterogeneity Discovery Pavel Shvaiko 2 nd European Semantic Web Conference (ESWC), 1 June 2005, Crete, Greece work in collaboration.

Web Explanations for Semantic Heterogeneity Discovery

Web Explanations for Semantic Heterogeneity Discovery

Pavel Shvaiko

2nd European Semantic Web Conference (ESWC),

1 June 2005, Crete, Greece

work in collaboration with Fausto Giunchiglia, Paulo Pinheiro da Silva and

Deborah L. McGuinness

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Outline

Introduction

Semantic Matching

Inference Web (IW) Framework

Explaining Semantic Matching using IW

Experimental Study

Conclusions

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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

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Semantic Matching

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Semantic Matching

Semantic Matching: Given two graphs G1 and G2, for any node n1i G1, find the strongest semantic relation R’ holding with node n2j 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

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Example: Two simple classifications

?

=Cyberspace and Virtual Reality

Italy

Europe

Pictures

Images

Europe

ItalyTrento

Computers and Internet

D.E.

A1 A2

Axioms rel (Context1, Context2)

(Images1Pictures2) (Europe1Europe2) (Images1 Europe1) (Europe2 Pictures2)

Axioms Context1 Context2

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S-Match

Expl.

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Inference Web (IW) Framework

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The IW Framework Overview

Inference Web is a framework enabling applications to generate portable and distributed explanations for their answers

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Explaining Semantic Matching

using IW

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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

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Default ExplanationA 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

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Explaining Background KnowledgeSuppose that the agent still does not trust the answer and may want to see the sources of metadata information behind the mapping

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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

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Experimental Study

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Preliminary Results

Goal: to obtain a vision of how the S-Match explanations potentially scale to requirements of the Semantic Web

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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

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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

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References

Project website at DIT - ACCORD: http://www.dit.unitn.it/~accord/

Project website at KSL - IW: 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.

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Thank you!


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