Service Semantic Link Network Discovery Based on Markov Structure

摘要：

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.

作者: Anping Zhao Zhixing Huang Yuhui Qiu

作者单位: Semantic Grid Lab, Faculty of Computer and Information Science,Southwest University Chongqing, China

会议类型: 国际会议

会议名称: Sixth International Conference on Semantics,Knowledge and Grids(第六届语义、知识与网格国际会议 SKG 2010)

会议地点: 宁波

会议语种:英文

页码: 9-16

在线出版日期: 2010-11-01（万方平台首次上网日期，不代表论文的发表时间）