TIAN Gang, WANG Jian, HE Keqing, et al., “Leveraging Auxiliary Knowledge for Web Service Clustering,” Chinese Journal of Electronics, vol. 25, no. 5, pp. 858-865, 2016, doi: 10.1049/cje.2016.06.008
Citation: TIAN Gang, WANG Jian, HE Keqing, et al., “Leveraging Auxiliary Knowledge for Web Service Clustering,” Chinese Journal of Electronics, vol. 25, no. 5, pp. 858-865, 2016, doi: 10.1049/cje.2016.06.008

Leveraging Auxiliary Knowledge for Web Service Clustering

doi: 10.1049/cje.2016.06.008
Funds:  This work is supported by the National Basic Research Program of China (973 Program) (No.2014CB340404), the National Natural Science Foundation of China (No.61202031, No.61373037), and the State Key Laboratory of Software Engineering Foundation (No.SKLSE 2014-10-07).
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  • Corresponding author: WANG Jian (corresponding author) was born in 1980 and is a lecturer atWuhan University. He is a member of China Computer Federation (CCF). His current research interests include requirement engineering and service computing. (Email:jianwang@whu.edu.cn)
  • Received Date: 2014-07-22
  • Rev Recd Date: 2014-09-23
  • Publish Date: 2016-09-10
  • By grouping Web services that share similar functionalities, Web service clustering can greatly enhance Web service discovery and selection. Most existing clustering techniques are designed to handle long text documents. However, the descriptions of most publicly available Web services are in the form of short text, which impairs the quality of service clustering due to the sparseness of useful information. Towards this issue, 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 and topics between service descriptions and auxiliary data, we introduce a novel topic model-Tag aided dual Author topical model (TD-ATM), which jointly learns two sets of topics on the two data sets and automatically couples the topic parameters to avoid the potential inconsistencies between these two data sets. Experimental results show the proposed approach outperforms several existing Web service clustering approaches.
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