Service Topic Model with Probability Distance

Abstract

The number of Web services are growing rapidly on the Internet. Topics of services are becoming various. Semantic-based keyword search is used to retrieve proper services for service consumers. According to the semantic information implied in service database, we build a topic model to cluster and management related services. Our service recommendation approach can extract service patterns from correlated topics in semantic service descriptions. We use Latent Dirichlet Allocation to obtain the service patterns, and use Concept lattice to model the correlation between the extracted topics. Higher precision results are obtained in the experiments.