Efficiently finding web services using a clustering semantic approach

  • Authors:

  • Jiangang Ma;Yanchun Zhang;Jing He

  • Affiliations:

  • Victoria University, Australia;Victoria University, Australia;Research Centre on Data Technology and Knowledge Economy, P.R. China

  • Venue:
  • Proceedings of the 2008 international workshop on Context enabled source and service selection, integration and adaptation: organized with the 17th International World Wide Web Conference (WWW 2008)
  • Year:
  • 2008

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Abstract

Efficiently finding Web services on the Web is a challenging issue in service-oriented computing. Currently, UDDI is a standard for publishing and discovery of Web services, and UDDI registries also provide keyword searches for Web services. However, the search functionality is very simple and fails to account for relationships between Web services. Firstly, users are overwhelmed by the huge number of irrelevant returned services. Secondly, the intentions of users and the semantics in Web services are ignored. Inspired by the success of partitioning approach used in the database design, we used a novel clustering semantic algorithm to eliminate irrelevant services with respect to a query. Then we utilized Probabilistic Latent Semantic Analysis (PLSA), a machine learning method, to capture the semantics hidden behind the words in a query, and the descriptions in the services, so that service matching can be carried out at the concept level. This paper reports upon the preliminary experimental evaluation that shows improvements over recall and precision.