Automatic XML Keyword Query Refinement
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Date
2009-06-23T02:36:06Z
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Abstract
Existing XML keyword search methods focus on
how to find relevant and meaningful data fragments for a keyword query, assuming each keyword is intended as part of it. However, user's queries usually contain irrelevant or mismatched terms, spelling errors etc, which causes the search results to be either empty or meaningless. In this paper, we introduce the
problem of automatic XML keyword query refinement, where automatic means the search engine should be able to adaptively decide whether a query Q needs to be refined during the
processing of Q, and at the same time find a list of promising refined query candidates and their matching results over XML data, without any user interaction or a second try. In order
to achieve this goal, we build a primary framework which consists of two core parts: (1) we build a novel query ranking model to evaluate the quality of a refined query RQ, which takes into account of the relevance of RQ w.r.t Q over XML data, the morphological/semantical similarity between Q and RQ, and the dependence of keywords of RQ in XML data. (2) We
integrate the exploration of RQ candidates and the generation of their matching results as a single problem at the same time of processing Q, which is fulfilled within a one-time scan of related keyword inverted lists optimally. Finally, an extensive empirical study verifies the efficiency and effectiveness of our framework.