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A Semantic-Based Approach to Service Clustering from Service Documents

With the rapid growth of service volumes and types, discovering services in an efficient and accurate manner has become a significant challenge in service computing. Service clustering is an important technology to improve the efficiency of service discovery. In this paper, we propose a new service clustering approach, which starts from service documents and is based on the functional semantics of services. This approach, first, extracts service goals from service description documents by using natural language processing technologies. Then it obtains the semantic similarity between two service goals and clusters the services by the K-means algorithm. Experiments conducted on a real-world service dataset crawled from ProgrammableWeb demonstrate the feasibility and the effectiveness of the proposed approach.