Service Semantic Link Network Discovery Based on Markov Structure

Abstract

Service Semantic Link Network (S-SLN) is the semantic model for effectively managing Web service resources by dependency relationship among services. In this paper, we provided an effective method for automatic discovering S-SLN based on graphical structure representation of the dependencies embedded in probability model. Markov network is an undirected graph whose links represent probability dependencies. We first learned Markov network structure from Web services data, and then transformed the undirected Markov network structure into directed graph structure of S-SLN based on the joint probability distribution. Finally, experimental results show the effectiveness of the method.