Leveraging Auxiliary Knowledge for Web Service Clustering

查看全文 作　　者：TIAN [1,2]Gang;WANG [1]Jian;HE [1]Keqing;SUN Cheng'[2]ai

机构地区：[1]State Key Laboratory of Software Engineering, School of Computer, Wuhan University;[2]College of Information and Science Engineering, Shandong University of Science and Technology

出　　处：《Chinese Journal of Electronics》索引2016年第25卷第5期,共8页

基　　金：supported by the National Basic Research Program ofChina(973 Program)(No.2014CB340404);the National Natural Science Foundation of China(No.61202031,No.61373037);the State Key Laboratory of Software Engineering Foundation(No.SKLSE 2014-10-07) 摘　　要：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. 关键词：WEB服务发现聚类技术知识文本文件聚类方法学习迁移局部模型服务质量

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Leveraging Auxiliary Knowledge for Web Service Clustering

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