Place semantics into context: service community discovery from the WSDL corpus
ICSOC'11 Proceedings of the 9th international conference on Service-Oriented Computing
Constrained co-clustering with non-negative matrix factorisation
International Journal of Business Intelligence and Data Mining
Sparse functional representation for large-scale service clustering
ICSOC'12 Proceedings of the 10th international conference on Service-Oriented Computing
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Efficient and accurate discovery of user desired Web services is a key component for achieving the full potential of service computing. However, service discovery is a non-trivial task considering the large and fast growing service space. Meanwhile, Web services are typically autonomous and a priori unknown. This further complicates the service discovery problem. We propose a service community learning algorithm that can generate homogeneous communities from the heterogeneous service space. This can greatly facilitate the service discovery process as the users only need to search within their desired service communities. A key ingredient of the community learning algorithm is a co-clustering scheme that leverages the duality relationship between services and operations. Experimental results on both synthetic and real Web services demonstrate the effectiveness of the proposed service community learning algorithm.