Title :
SAWSDL-MX2: A Machine-Learning Approach for Integrating Semantic Web Service Matchmaking Variants
Author :
Klusch, Matthias ; Kapahnke, Patrick ; Zinnikus, Ingo
Author_Institution :
German Res. Center for Artificial Intell., Saarbrucken, Germany
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.
Keywords :
Web services; information retrieval; learning (artificial intelligence); performance evaluation; semantic Web; support vector machines; SAWSDL-MX2; aggregation function; hybrid semantic Web service matchmaker; machine learning approach; model references; nonlinear kernel; performance evaluation; structural approach; support vector machine; syntactic description; text similarity service selection; Artificial intelligence; Buildings; Impedance matching; Kernel; Matched filters; Semantic Web; Support vector machines; Testing; Training data; Web services; SAWSDL; matchmaking;
Conference_Titel :
Web Services, 2009. ICWS 2009. IEEE International Conference on
Conference_Location :
Los Angeles, CA
Print_ISBN :
978-0-7695-3709-2
DOI :
10.1109/ICWS.2009.76