[Objective] We propose an effective method to cluster and discover the needed Web services. [Methods] First, we employed the Biterm Topic Model to learn the latent topics of the Web service description corpus. Second, we retrieved and clustered each document’s topic distribution. Finally, we created a mechanism to discover Web service quickly. [Results] The proposed method achieved better precision rate and normalized discounted cumulative gain than methods using Latent Dirichlet Allocation and external corpus. [Limitations] Only considered functions of the Web services, and did not include the quality factors to the algorithm. [Conclusions] The proposed method could identify the needed services more accurately.
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