A Deep Web Query Interface Matching Approach Based on Evidence Theory and Task Assignment
DONG Yong-Quan1,2, LI Qing-Zhong1, DING Yan-Hui1, Zhang Yong-Xin1
1School of Computer Science and Technology, Shandong University, Jinan 250101 2School of Computer Science and Technology, Xuzhou Normal University, Xuzhou 221006
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 is proposed called evidence theory and task assignment based query interface matching approach(ETTA-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 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.
DONG Yong-Quan,LI Qing-Zhong,DING Yan-Hui等. A Deep Web Query Interface Matching Approach Based on Evidence Theory and Task Assignment[J]. , 2011, 24(2): 262-271.
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