Leveraging Auxiliary Knowledge for Web Service Clustering  

作　　者：TIAN Gang WANG Jian HE Keqing SUN Cheng'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》2016年第5期858-865,共8页电子学报（英文版）

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.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 service clustering Tags Topic model Transfer learning 

分 类 号：TP393.09[自动化与计算机技术—计算机应用技术]