Web services clustering based on Biterm topic model

Computer Engineering & Science

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Web services clustering based on Biterm topic model

CHEN Ting1,2,LIU Jianxun1,2,CAO Buqing1,2,LI Run2

(1.Key Laboratory of Knowledge Processing & Networked Manufacturing,

Hunan University of Science and Technology,Xiangtan 411201;

2.School of Computer Science and Engineering,Hunan University of Science and Technology,Xiangtan 411201,China)

Received:2018-05-11 Revised:2018-07-18 Online:2018-10-25 Published:2018-10-25

Abstract

Abstract:

It is not ideal for the huge number of Web services with data sparseness to use traditional modeling methods to cluster them. To solve this problem, we present a Web service clustering method based on the Biterm topic model (BTM). This method firstly employs the BTM to learn the latent topics of Web service description corpus. Secondly, it derives the topic distribution of each Web service. Finally, it uses the K-Means algorithm to cluster Web services. Compared with the LDA and TF-IDF clustering methods, the proposed approach achieves better performance in purity, entropy and F-measure. Our method can effectively solve the data sparseness problem caused by the short text nature of Web service description, and significantly improve service clustering.

Key words: Web service, Biterm topic model, short text, Web services clustering

CHEN Ting1,2,LIU Jianxun1,2,CAO Buqing1,2,LI Run2.

Web services clustering based on Biterm topic model

[J]. Computer Engineering & Science.

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Web services clustering based on Biterm topic model

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