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The Creation and Evaluation of iSPARQL Strategies for Matchmaking

  • Christoph Kiefer
  • Abraham Bernstein
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5021)

Abstract

This research explores a new method for Semantic Web service matchmaking based on iSPARQL strategies, which enables to query the Semantic Web with techniques from traditional information retrieval. The strategies for matchmaking that we developed and evaluated can make use of a plethora of similarity measures and combination functions from SimPack—our library of similarity measures. We show how our combination of structured and imprecise querying can be used to perform hybrid Semantic Web service matchmaking. We analyze our approach thoroughly on a large OWL-S service test collection and show how our initial strategies can be improved by applying machine learning algorithms to result in very effective strategies for matchmaking.

Keywords

Service Description Triple Pattern Relevant Service Approximate Match Selectivity Estimation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Christoph Kiefer
    • 1
  • Abraham Bernstein
    • 1
  1. 1.Department of InformaticsUniversity of ZurichSwitzerland

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