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Abstract 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 DS 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 onetomany matching decision. Experimental results show that ETTA-IM approach has high precision and recall measure.
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