Co-clustering WSDL Documents to Bootstrap Service Discovery

With the increasing popularity of web service, it is indispensable to efficiently locate the desired service. Utilizing WSDL documents to cluster web services into functionally similar service groups is becoming mainstream in recent years. However, most existing algorithms cluster WSDL documents solely and ignore the distribution of words rather than cluster them simultaneously. Different from the traditional clustering algorithms that are on one-way clustering, this paper proposes a novel approach named WCCluster to simultaneously cluster WSDL documents and the words extracted from them to improve the accuracy of clustering. WCCluster poses co-clustering as a bipartite graph partitioning problem, and uses a spectral graph algorithm in which proper singular vectors are utilized as a real relaxation to the NP-complete graph partitioning problem. To evaluate the proposed approach, we design comprehensive experiments based on a real-world data set, and the results demonstrate the effectiveness of WCCluster.