ADMIRE: an adaptive data model for meta search engines

Abstract Considering the diversity among search engines, efficient integration of them is an important but difficult job. It is essential to provide a data model that can provide a detailed description of the query capabilities of heterogeneous search engines. By means of this model, the meta-searcher can map users' queries into specific sources more accurately, and it can achieve good precision and recall. Moreover, it will benefit the selection of target source and computing priority. Because new search engines emerge frequently and old ones are updated when their function and content change, the data model needs good adaptivity and scalability to keep in step with the rapidly developing World Wide Web. This paper gives a formal description of the query capabilities of heterogeneous search engines and an algorithm for mapping a query from a general mediator format into the specific wrapper format of a specific search engine. Compared with related work, the special features of our work are that we focus more on the constraint of/between the terms, attribute order, and the impact of logical operator restraints. The contribution of our work is that we offer a data model that is both expressive enough to meticulously describe the query capabilities of current World Wide Web search engines and flexible enough to integrate them efficiently.

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