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With an ever-increasing number of Web services being available, finding desired Web service is crucial for service users. Current keyword search and most existing approaches are inefficient in two main aspects: poor scalability and lack of semantics. Firstly, users are overwhelmed by the huge number of irrelevant services returned. Secondly, the intentions of users and the semantics in Web services are ignored. Inspired by the success of the divide and conquer approach used to handle the complex information decomposition, we use a novel approach to partition a large set of search results into a set of smaller groups by employing a clustering approach. Then we utilize singular value decomposition (SVD) to capture the main semantics hidden behind the words in a query and the descriptions in the services, so that service matching can be carried out at the concept level. We report here on the preliminary experimental evaluation that shows improvements overall precision.