Leveraging Auxiliary Knowledge for Web Service Clustering

• Published in: Chinese Journal of Electronics 2016-09, Vol.25 (5), p.858-865
• Main Authors: Tian, Gang, Wang, Jian, He, Keqing, Sun, Cheng'ai
• Format: Article
• Language: English
• Subjects: long text documents; pattern clustering; service oriented computing; service‐oriented architecture; SOC; tag aided dual author topical model; Tags; TD‐ATM; text analysis; Topic model; Transfer learning; Web service clustering; Web service clustering approach; Web service discovery; Web service selection; Web services; Web sites; Web服务发现; Wikipedia; 学习迁移; 局部模型; 文本文件; 服务质量; 知识; 聚类技术; 聚类方法
• ISSN: 1022-4653; 2075-5597
• DOI: 10.1049/cje.2016.06.008

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.