Correlation Search of Web Services

With the development of services computing and cloud computing, number of Web services has increased rapidly, and it becomes quite popular for developers to combine different Web services to build innovative Mash up applications. How to quickly locate desired Web service for developers, however, is still a challenging problem that needs to be addressed. Most existing related work employed keyword-based method to search services and focused on matching users' queries with semantic or syntactic Web service description. They seldom took advantage of relationships between services to improve the performance of service searching. This paper presents a correlation search method by making use of several relationships between Web services, to recommend a user with services that are similar, composable or potentially composable to a target service. One important advantage of this method is that it can guide users to find desired services promptly, and thus improves efficiency of the service discovery process. To mine the different relationships between services, several efficient algorithms are presented. Case studies and experiments show, the above correlation search method not only can recommend Web services to users that are relevant to the users' interest, but also can predict composable relationships between services with high performance.

[1]  Linyuan Lu,et al.  Link Prediction in Complex Networks: A Survey , 2010, ArXiv.

[2]  Zhigang Chen,et al.  A Method for Semantic Web Service Selection Based on QoS Ontology , 2011, J. Comput..

[3]  Linyuan Lü,et al.  Predicting missing links via local information , 2009, 0901.0553.

[4]  Linyuan Lü,et al.  Similarity index based on local paths for link prediction of complex networks. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[5]  Hee Yong Youn,et al.  A Novel Semantic Web Service Discovery Scheme Using Bipartite Graph , 2013, 2013 IEEE 10th International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing.

[6]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[7]  Michael McGill,et al.  Introduction to Modern Information Retrieval , 1983 .

[8]  Zibin Zheng,et al.  Titan: a system for effective web service discovery , 2012, WWW.

[9]  Mario A. Nascimento,et al.  Proceedings of the Thirtieth international conference on Very large data bases - Volume 30 , 2004 .

[10]  Zhou Shuigeng Web Services Search Techniques:A Survey , 2010 .

[11]  Philip S. Yu,et al.  PathSim , 2011, Proc. VLDB Endow..

[12]  Jun Zhang,et al.  Simlarity Search for Web Services , 2004, VLDB.

[13]  Lin Huiping Semantic Based Service Discovery and Matching Method , 2011 .

[14]  Mingdong Tang,et al.  An Effective Web Service Recommendation Method Based on Personalized Collaborative Filtering , 2011, 2011 IEEE International Conference on Web Services.

[15]  Jun Zhang,et al.  HyperService: Linking and Exploring Services on the Web , 2010, 2010 IEEE International Conference on Web Services.