Transferring auxiliary knowledge to enhance heterogeneous web service clustering

The growing number of web services puts forward higher requirements for searching desired web services, and clustering web services can greatly enhance the discovery of web service. Most existing clustering approaches are designed to handle long text documents. However, the descriptions of most services are in the form of short text, which impairs the quality of clustering owing to the lack of statistical information. To solve this problem, we propose a new service clustering approach based on transfer learning from auxiliary long text data obtained from Wikipedia. To handle the inconsistencies in semantics between service descriptions and auxiliary data, we introduce a novel topic model – dual tag aided latent Dirichlet allocation (DT-LDA), which jointly learns two sets of topics on the two datasets. Experimental results show the proposed approach achieves better performance than several existing approaches.