Discovering semantic associations among Web services based on the qualitative probabilistic network

  • Authors:

  • Kun Yue;Weiyi Liu;Xiaoling Wang;Aoying Zhou;Jin Li

  • Affiliations:

  • Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming 650091, PR China;Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming 650091, PR China;Institute of Massive Computing, East China Normal University, Shanghai 200062, PR China;Institute of Massive Computing, East China Normal University, Shanghai 200062, PR China;Department of Software Engineering, School of Software, Yunnan University, Kunming 650091, PR China

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2009

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Abstract

In recent years, the intelligent management and decision of Web services have attracted more and more attention due to the wide applications in various aspects of the real world. With the increase of Web services in an organization, the desired on-line services should be located rapidly requiring not only the syntactic but also the semantic techniques. In addition, aiming at fulfilling complex applications by discovering and composing available services automatically and precisely, it is indispensable to develop an underlying model and the corresponding measure for semantic associations among given Web services. In this paper, by mining the historical invocations of component services, we first construct a semantic model to describe their behavior rules based on the qualitative probabilistic network. Further, we propose a distance measure and the approach to discovering semantic associations among Web services. Preliminary experiments and performance studies show that our methods are feasible. Moreover, high recall and precision can be achieved when our methods are applied to Web service search.