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Race: finding and ranking compact connected trees for keyword proximity search over xml documents
Proceedings of the 17th international conference on World Wide Web
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BNCOD '08 Proceedings of the 25th British national conference on Databases: Sharing Data, Information and Knowledge
An effective and versatile keyword search engine on heterogenous data sources
Proceedings of the VLDB Endowment
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An effective 3-in-1 keyword search method over heterogeneous data sources
Information Systems
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The VLDB Journal — The International Journal on Very Large Data Bases
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This paper studies the problem of unified ranked retrieval of heterogeneous XML documents and Web data. We propose an effective search engine called Sailer to adaptively and versatilely answer keyword queries over the heterogenous data. We model the Web pages and XML documents as graphs. We propose the concept of pivotal trees to effectively answer keyword queries and present an effective method to identify the top-k pivotal trees with the highest ranks from the graphs. Moreover, we propose effective indexes to facilitate the effective unified ranked retrieval. We have conducted an extensive experimental study using real datasets, and the experimental results show that Sailer achieves both high search efficiency and accuracy, and outperforms the existing approaches significantly.