iSeM: Approximated Reasoning for Adaptive Hybrid Selection of Semantic Services

Matthias Klusch, Patrick Kapahnke

In: Proceedings of the 4th IEEE International Conference on Semantic Computing. IEEE International Conference on Semantic Computing (ICSC-2010) 4th September 22-24 Pittsburgh PA United States Springer-Verlag 2010.


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 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.

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence