Context Aware Filtering and Visualization of Web Service Clusters
Web service filtering is an efficient approach to address some big challenges in service computing, such as discovery, clustering and recommendation. The key operation of the filtering process is measuring the similarity of services. Several methods are used in current similarity calculation approaches such as string based, corpus based, knowledge based and hybrid methods. These approaches do not consider domain specific contexts in measuring similarity because they have failed to capture the semantic similarity of Web services in a given domain and this has affected their filtering performance. In this paper, we propose a context aware similarity method that uses a support vector machine and a domain dataset from a context specific search engine query. Our filtering approach uses a spherical associated keyword space algorithm that projects filtering results from a three dimensional sphere to a two dimensional (2D) spherical surface for 2D visualization. Experimental results show that our filtering approach works efficiently.
2014 IEEE International Conference on Web Services