Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-12026-8_10
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dc.titleAn effective object-level XML keyword search
dc.contributor.authorBao, Z.
dc.contributor.authorLu, J.
dc.contributor.authorLing, T.W.
dc.contributor.authorXu, L.
dc.contributor.authorWu, H.
dc.date.accessioned2013-07-04T08:05:18Z
dc.date.available2013-07-04T08:05:18Z
dc.date.issued2010
dc.identifier.citationBao, Z.,Lu, J.,Ling, T.W.,Xu, L.,Wu, H. (2010). An effective object-level XML keyword search. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 5981 LNCS (PART 1) : 93-109. ScholarBank@NUS Repository. <a href="https://doi.org/10.1007/978-3-642-12026-8_10" target="_blank">https://doi.org/10.1007/978-3-642-12026-8_10</a>
dc.identifier.isbn3642120253
dc.identifier.issn03029743
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/40481
dc.description.abstractKeyword search is widely recognized as a convenient way to retrieve information from XML data. In order to precisely meet users' search concerns, we study how to effectively return the targets that users intend to search for. We model XML document as a set of interconnected object-trees, where each object contains a subtree to represent a concept in the real world. Based on this model, we propose object-level matching semantics called Interested Single Object (ISO) and Interested Rel ated Object (IRO) to capture single object and multiple objects as user's search targets respectively, and design a novel relevance oriented ranking framework for the matching results. We propose efficient algorithms to compute and rank the query results in one phase. Finally, comprehensive experiments show the efficiency and effectiveness of our approach, and an online demo of our system on DBLP data is available at http://xmldb.ddns.comp.nus.edu.sg. © Springer-Verlag Berlin Heidelberg 2010.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/978-3-642-12026-8_10
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1007/978-3-642-12026-8_10
dc.description.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.description.volume5981 LNCS
dc.description.issuePART 1
dc.description.page93-109
dc.identifier.isiutNOT_IN_WOS
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