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Bayes Decision Method for Semantic Web Service Discovery

Xingjiang Yang, Xiangbing Zhou, Hongjiang Ma


Semantic Web service is mainly aimed at the service location and the service aggregation in distributed cases. It mainly works on the service discovery and service composition, but we can still find some defects in precise semantic knowledge structure, service positioning and service efficiency. In allusion to the precise semantic knowledge structure, we propose a construction method for semantic Web service discovery mechanism in this paper. We access to knowledge and resources through the navigation property of the topic map, use the body to realize the semantic recognition and extraction between services. Also we adopt a knowledge building automata to create a service discovery mechanism based on Bayesian decision theory. We can obtain to optimize service discovery model so as to achieve the best service. Ultimately, through the WordNet experimental analysis, this method is proved to be feasible and effective.


Bayesian Decision, Semantic Web Services, Ontology, Markov.

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