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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.

iSeM: Approximated Reasoning for Adaptive Hybrid Selection of Semantic Services