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  • No-but-semantic-match: computing semantically matched xml keyword search results

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    Author(s)
    Naseriparsa, Mehdi
    Islam, Md Saiful
    Liu, Chengfei
    Moser, Irene
    Griffith University Author(s)
    Islam, Saiful
    Year published
    2018
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    Abstract
    Users are rarely familiar with the content of a data source they are querying, and therefore cannot avoid using keywords that do not exist in the data source. Traditional systems may respond with an empty result, causing dissatisfaction, while the data source in effect holds semantically related content. In this paper we study this no-but-semantic-match problem on XML keyword search and propose a solution which enables us to present the top-k semantically related results to the user. Our solution involves two steps: (a) extracting semantically related candidate queries from the original query and (b) processing candidate ...
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    Users are rarely familiar with the content of a data source they are querying, and therefore cannot avoid using keywords that do not exist in the data source. Traditional systems may respond with an empty result, causing dissatisfaction, while the data source in effect holds semantically related content. In this paper we study this no-but-semantic-match problem on XML keyword search and propose a solution which enables us to present the top-k semantically related results to the user. Our solution involves two steps: (a) extracting semantically related candidate queries from the original query and (b) processing candidate queries and retrieving the top-k semantically related results. Candidate queries are generated by replacement of non-mapped keywords with candidate keywords obtained from an ontological knowledge base. Candidate results are scored using their cohesiveness and their similarity to the original query. Since the number of queries to process can be large, with each result having to be analyzed, we propose pruning techniques to retrieve the top-k results efficiently. We develop two query processing algorithms based on our pruning techniques. Further, we exploit a property of the candidate queries to propose a technique for processing multiple queries in batch, which improves the performance substantially. Extensive experiments on two real datasets verify the effectiveness and efficiency of the proposed approaches.
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    Journal Title
    World Wide Web
    DOI
    https://doi.org/10.1007/s11280-017-0503-8
    Copyright Statement
    © 2017 Springer Netherlands. This is an electronic version of an article published in World Wide Web, 2017, pp 1–35. World Wide Web is available online at: http://link.springer.com/ with the open URL of your article.
    Note
    This publication has been entered into Griffith Research Online as an Advanced Online Version.
    Subject
    Data management and data science
    Distributed computing and systems software
    Information systems
    Information systems not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/350464
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    • Journal articles

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