GIT-CERCS-03-26
James Caverlee, Ling Liu, Daniel Rocco,
Discovering and Ranking Data Intensive Web Services: A Source-Biased Approach
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.