Discovering and ranking web services with BASIL: a personalized approach with biased focus

  • Authors:
  • James Caverlee;Ling Liu;Daniel Rocco

  • Affiliations:
  • Georgia Institute of Technology, Atlanta, GA;Georgia Institute of Technology, Atlanta, GA;Georgia Institute of Technology, Atlanta, GA

  • Venue:
  • Proceedings of the 2nd international conference on Service oriented computing
  • Year:
  • 2004

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Abstract

In this paper we present a personalized web service discovery and ranking technique for discovering and ranking relevant data-intensive web services. Our first prototype -- called BASIL -- supports a personalized view of data-intensive web services through source-biased focus. BASIL provides service discovery and ranking through source-biased probing and source-biased relevance metrics. Concretely, the BASIL approach has three unique features: (1) It is able to determine in very few interactions whether a target service is relevant to the given source service by probing the target with very precise probes; (2) It can evaluate and rank the relevant services discovered based on a set of source-biased relevance metrics; and (3) It can identify interesting types of relationships for each source service with respect to other discovered services, which can be used as value-added metadata for each service. We also introduce a performance optimization technique called source-biased probing with focal terms to further improve the effectiveness of the basic source-biased service discovery algorithm. The paper concludes with a set of initial experiments showing the effectiveness of the BASIL system.