A logistic regression model for Semantic Web service matchmaking  

作　　者：WEI DengPing WANG Ting WANG Ji 

机构地区：[1]School of Computer,National University of Defense Technology,Changsha 410073,China [2]National Laboratory for Parallel and Distributed Processing,Changsha 410073,China

出　　处：《Science China(Information Sciences)》2012年第7期1715-1720,共6页中国科学（信息科学）（英文版）

基　　金：supported by National Grand Fundamental Research Program of China (Grant No. 2011CB30-2603);National Natural Science Foundation of China (Grant No. 60873097)

摘　　要：Semantic Web service matchmaking, as one of the most challenging problems in Semantic Web services （SWS）, aims to filter and rank a set of services with respect to a service query by using a certain matching strategy. In this paper, we propose a logistic regression based method to aggregate several matching strategies instead of a fixed integration （e.g., the weighted sum） for SWS matchmaking. The logistic regression model is trained on training data derived from binary relevance assessments of existing test collections, and then used to predict the probability of relevance between a new pair of query and service according to their matching values obtained from various matching strategies. Services axe then ranked according to the probabilities of relevance with respect to each query. Our method is evaluated on two main test collections, SAWSDL-TC2 and Jena Geography Dataset（JCD）. Experimental results show that the logistic regression model can effectively predict the relevance between a query and a service, and hence can improve the effectiveness of service matchmaking.Semantic Web service matchmaking, as one of the most challenging problems in Semantic Web services （SWS）, aims to filter and rank a set of services with respect to a service query by using a certain matching strategy. In this paper, we propose a logistic regression based method to aggregate several matching strategies instead of a fixed integration （e.g., the weighted sum） for SWS matchmaking. The logistic regression model is trained on training data derived from binary relevance assessments of existing test collections, and then used to predict the probability of relevance between a new pair of query and service according to their matching values obtained from various matching strategies. Services axe then ranked according to the probabilities of relevance with respect to each query. Our method is evaluated on two main test collections, SAWSDL-TC2 and Jena Geography Dataset（JCD）. Experimental results show that the logistic regression model can effectively predict the relevance between a query and a service, and hence can improve the effectiveness of service matchmaking.

关 键 词：Semantic Web service MATCHMAKING logistic regression 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论] O212.1[自动化与计算机技术—计算机科学与技术]