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Publication

SAWSDL-MX2: A Machine-Learning Approach for Integrating Semantic Web Service Matchmaking Variants

Matthias Klusch; Patrick Kapahnke; Ingo Zinnikus
In: Ernesto Damiani; Rong Chang; Jia Zhang (Hrsg.). 2009 IEEE International Conference on Web Services. IEEE International Conference on Web Services (ICWS-2009), 7th, July 6-10, Los Angeles, CA, USA, Pages 335-342, ISBN 978-0-7695-3709-2, IEEE Press, 2009.

Abstract

In this paper, we present SAWSDL-MX2, a hybrid semantic Web service matchmaker for SAWSDL services. Building on our initial work, we adopt logic-based as well as text similarity service selection for model references and add a structural approach, which operates on the pure syntactic description of WSDL elements. The integration of these matching variants is accomplished using a Support Vector Machine (SVM) with non-linear kernel, thus automatically adapting an aggregation function based on previously experienced training data. Results of our performance evaluation based on the standard measures recall and precision over the SAWSDL-TC1 test collection as well as an exhaustive example for all basic matching variants are also given.