XRANK: ranked keyword search over XML documents
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Keyword Proximity Search in XML Trees
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Effective keyword search for valuable lcas over xml documents
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Exploit keyword query semantics and structure of data for effective XML keyword search
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An effective 3-in-1 keyword search method over heterogeneous data sources
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Providing built-in keyword search capabilities in RDBMS
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Adaptive and effective keyword search for XML
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In this paper, we study the problem of keyword proximity search over XML documents and leverage the efficiency and effectiveness. We take the disjunctive semantics among input keywords into consideration and identify meaningful compact connected trees as the answers of keyword proximity queries. We introduce the notions of Compact Lowest Common Ancestor (CLCA) and Maximal CLCA (MCLCA) and propose Compact Connected Trees (CCTrees) and Maximal CCTrees (MCCTrees) to efficiently and effectively answer keyword queries. We propose a novel ranking mechanism, RACE, to Rank compAct Connected trEes, by taking into consideration both the structural similarity and the textual similarity. Our extensive experimental study shows that our method achieves both high search efficiency and effectiveness, and outperforms existing approaches significantly.