Discovering and Ranking Data Intensive Web Services: A Source-Biased Approach

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Caverlee, James
Liu, Ling
Rocco, Daniel J. (Daniel John)
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This paper presents a novel source-biased approach to automatically discover and rank relevant data intensive web services. It supports a service-centric view of the Web through source-biased probing and source-biased relevance detection and ranking metrics. Concretely, our approach is capable of answering source-centric queries by focusing on the nature and degree of the topical relevance of one service to others. This source-biased probing allows us to determine in very few interactions whether a target service is relevant to the source by probing the target with very precise probes and then ranking the relevant services discovered based on a set of metrics we define. Our metrics allow us to determine the nature and degree of the relevance of one service to another. We also introduce a performance enhancement to our basic approach called source-biased probing with focal terms. We also extend the basic probing framework to a more generalized service neighborhood graph model. We discuss the semantics of the neighborhood graph, how we may reason about the relationships among multiple services, and how we rank services based on the service neighborhood graph model. We also report initial experiments to show the effectiveness of our approach.
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