ETTA-IM: A deep web query interface matching approach based on evidence theory and task assignment

Integrating Deep Web data sources require highly accurate matches between the attributes of the query interfaces. While interface matching has received more attentions recently, current approaches are still not sufficiently perfect: (a) they all suppose that every interface attribute type has been predefined; (b) most of them combine multiple matchers taking into account different aspects of information about schema, but the weights of individual matchers are usually manually generated, and there may exist a high degree of inconsistency among different matchers; and (c) most of them only consider one-to-one matches of attributes over the interfaces and lack effective mathematical modeling. Therefore, a novel deep web query interface matching approach called ETTA-IM is proposed based on evidence theory and task assignment. Varied kinds of type recognizers are defined to identify the types of interface attributes which are used to divide the schema space into several schema subspaces. A modified D-S evidence theory is used to automatically combine multiple matchers and to solve high conflicts among different matchers. One-to-one match decision is converted to extended task assignment problem and some tree structure heuristic rules are used to perform one-to-many match decision. Experiments show that ETTA-IM approach yields high precision and recall measures.