Please use this identifier to cite or link to this item: https://doi.org/10.1109/TKDE.2010.63
DC FieldValue
dc.titleTowards an effective XML keyword search
dc.contributor.authorBao, Z.
dc.contributor.authorLu, J.
dc.contributor.authorLing, T.W.
dc.contributor.authorChen, B.
dc.date.accessioned2013-07-23T09:25:54Z
dc.date.available2013-07-23T09:25:54Z
dc.date.issued2010
dc.identifier.citationBao, Z., Lu, J., Ling, T.W., Chen, B. (2010). Towards an effective XML keyword search. IEEE Transactions on Knowledge and Data Engineering 22 (8) : 1077-1092. ScholarBank@NUS Repository. https://doi.org/10.1109/TKDE.2010.63
dc.identifier.issn10414347
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/43134
dc.description.abstractInspired by the great success of information retrieval (IR) style keyword search on the web, keyword search on XML has emerged recently. The difference between text database and XML database results in three new challenges: 1) Identify the user search intention, i.e., identify the XML node types that user wants to search for and search via. 2) Resolve keyword ambiguity problems: a keyword can appear as both a tag name and a text value of some node; a keyword can appear as the text values of different XML node types and carry different meanings; a keyword can appear as the tag name of different XML node types with different meanings. 3) As the search results are subtrees of the XML document, new scoring function is needed to estimate its relevance to a given query. However, existing methods cannot resolve these challenges, thus return low result quality in term of query relevance. In this paper, we propose an IR-style approach which basically utilizes the statistics of underlying XML data to address these challenges. We first propose specific guidelines that a search engine should meet in both search intention identification and relevance oriented ranking for search results. Then, based on these guidelines, we design novel formulae to identify the search for nodes and search via nodes of a query, and present a novel XML TF *IDF ranking strategy to rank the individual matches of all possible search intentions. To complement our result ranking framework, we also take the popularity into consideration for the results that have comparable relevance scores. Lastly, extensive experiments have been conducted to show the effectiveness of our approach. © 2010 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/TKDE.2010.63
dc.sourceScopus
dc.subjectkeyword search
dc.subjectranking
dc.subjectXML
dc.typeArticle
dc.contributor.departmentCOMPUTER SCIENCE
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1109/TKDE.2010.63
dc.description.sourcetitleIEEE Transactions on Knowledge and Data Engineering
dc.description.volume22
dc.description.issue8
dc.description.page1077-1092
dc.description.codenITKEE
dc.identifier.isiut000279060600003
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