一种基于证据理论和任务分配的DeepWeb查询接口匹配方法

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。

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

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 called Evidence Theory and Task Assignment based Query Interface Matching Approach(ETTA-IM) is proposed. Firstly, an improved D S evidence theory is used to automatically combine multiple matchers. In this way, 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 matching decision to the extended task assignment problem. Finally, based on one to one matching results, some heuristic rules of tree structure are used to perform one to many matching decision. Experimental results show that ETTA-IM approach has high precision and recall measure.