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iSeM: Approximated Reasoning for Adaptive Hybrid Selection of Semantic Services

  • Matthias Klusch
  • Patrick Kapahnke
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6089)

Abstract

We present an intelligent service matchmaker, called iSeM, for adaptive and hybrid semantic service selection that exploits the full semantic profile in terms of signature annotations in description logic \({\mathcal SH}\) and functional specifications in SWRL. In particular, iSeM complements its strict logical signature matching with approximated reasoning based on logical concept abduction and contraction together with information-theoretic similarity and evidential coherence-based valuation of the result, and non-logic-based approximated matching. Besides, it may avoid failures of signature matching only through logical specification plug-in matching of service preconditions and effects. Eventually, it learns the optimal aggregation of its logical and non-logic-based matching filters off-line by means of binary SVM-based service relevance classifier with ranking. We demonstrate the usefulness of iSeM by example and preliminary results of experimental performance evaluation.

Keywords

Description Logic Service Selection Signature Match Informative Quality Binary Relevance 
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 2010

Authors and Affiliations

  • Matthias Klusch
    • 1
  • Patrick Kapahnke
    • 1
  1. 1.German Research Center for Artificial IntelligenceSaarbrückenGermany

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