一种基于证据理论和任务分配的Deep Web查询接口匹配方法  

A Deep Web Query Interface Matching Approach Based on Evidence Theory and Task Assignment

作　　者：董永权[1,2] 李庆忠[1] 丁艳辉[1] 张永新[1] 

机构地区：[1]山东大学计算机科学与技术学院,济南250101 [2]徐州师范大学计算机科学与技术学院,221006

出　　处：《模式识别与人工智能》2011年第2期262-271,共10页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.90818001);山东省自然科学基金项目(No.Y2007G24)资助

摘　　要：针对已有查询接口匹配方法匹配器权重设置困难、匹配决策缺乏有效处理的局限性,提出一种基于证据理论和任务分配的DeepWeb查询接口匹配方法.该方法通过引入改进的D-S证据理论自动融合多个匹配器结果,避免手工设定匹配器权重,有效减少人工干预.通过对任务分配问题进行扩展,将查询接口的一对一匹配决策问题转化为扩展的任务分配问题,为源查询接口中的每一个属性选择合适的匹配,并在此基础上,采用树结构启发式规则进行一对多匹配决策.实验结果表明ETTA-IM方法具有较高的查准率和查全率.To solve the limitations of existing query interface matching which have the difficulties of weight setting of the matcher and the absence of the efficient processing of matching decision, a deep web query interface matching approach based on evidence theory and task assignment is proposed called evidence theory and task assignment based query interface matching approach （ETFA-IM）. Firstly, an improved D-S evidence theory is used to automatically combine multiple matchers. Thus, the weight of each matcher is not required to be set by hand and human involvement is reduced. Then, a method is used to select a proper attribute correspondence of each source attribute from target query interface, which converts one-to-one one-to-one matching matching decision to the extended task assignment problem. Finally, based on results, some heuristic rules of tree structure are used to perform one-to-manymatching decision. Experimental results show that ETFA-IM approach has high precision and recall measure.

关 键 词：查询接口匹配 模式匹配 DEEP Web WEB数据集成 

分 类 号：TP393.09[自动化与计算机技术—计算机应用技术]