COMPOSITION CONTEXT BASED WEB SERVICES SIMILARITY 0MEASURE

Abstract

Web services similarity measure is an important problem in service computing area, which is the
technological foundation of service substitution, service discovery, service recommendation, etc. Most of
existing works use static description of services to measure the similarity between two services. However, the
interaction information of Web services recorded in the historical compositions is totally neglected. In this
paper, we propose a novel Web services similarity measure approach based on the notion of service composition
context. Specifically, we first introduce three types of parameter correlations between service input and output
parameters. These correlations can be obtained from existing services compositions. Based on parameter
correlations, we propose the service composition context model. Through the composition context of a service,
the composition context network is constructed using contexts of all services. Then, we propose to measure the
similarity between any two services using the PersonalRank and SimRank++ algorithm by taking the obtained
context network as input. By experiments, we analyze characteristics of our proposed method, and demonstrate
that its accuracy is much better than the state-of-the-art approaches.