Adaptive Hybrid Selection of Semantic Services: The iSeM Matchmaker

Chapter

Abstract

We present the intelligent service matchmaker iSeM, which exhaustively exploits functional service descriptions in terms of logical signature annotations in OWL and specifications of preconditions and effects in SWRL. In particular, besides strict logical matching filters, text and structural similarity, it adopts approximated reasoning based on logical concept abduction and contraction for the description logic subset SH with information-theoretic valuation for matching inputs and outputs. In addition, it uses stateless logical specification matching in terms of the incomplete but decidable \(\theta \)-subsumption algorithm for preconditions and effects. The optimal aggregation strategy of the above mentioned matching aspects is adapted off-line by means of a binary SVM-based service relevance classifier in combination with evidential coherence-based pruning to improve ranking precision with respect to false classification of any such variant on its own. We demonstrate the additional benefit of the presented approximation and the adaptive hybrid combination by example and by presenting an experimental performance analysis.

Keywords

Average Precision Service Selection Signature Match Query Response Time Signature Concept 
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 2012

Authors and Affiliations

  1. 1.German Research Center for Artificial Intelligence (DFKI)SaarbrueckenGermany

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