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  • XPloreRank: exploring XML data via you may also like queries

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    NaseriparsaPUB5336.pdf (4.024Mb)
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    Accepted Manuscript (AM)
    Author(s)
    Naseriparsa, Mehdi
    Liu, Chengfei
    Islam, Md Saiful
    Zhou, Rui
    Griffith University Author(s)
    Islam, Saiful
    Year published
    2019
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    Abstract
    In many cases, users are not familiar with their exact information needs while searching complicated data sources. This lack of understanding may cause the users to feel dissatisfaction when the system retrieves insufficient results after they issue queries. However, using their original query results, we may recommend additional queries which are highly relevant to the original query. This paper presents XPloreRank to recommend top-l highly relevant keyword queries called “You May Also Like” (YMAL) queries to the users in XML keyword search. To generate such queries, we firstly analyze the original keyword query results ...
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    In many cases, users are not familiar with their exact information needs while searching complicated data sources. This lack of understanding may cause the users to feel dissatisfaction when the system retrieves insufficient results after they issue queries. However, using their original query results, we may recommend additional queries which are highly relevant to the original query. This paper presents XPloreRank to recommend top-l highly relevant keyword queries called “You May Also Like” (YMAL) queries to the users in XML keyword search. To generate such queries, we firstly analyze the original keyword query results content and construct a weighted co-occurring keyword graph. Then, we generate the YMAL queries by traversing the co-occurring keyword graph and rank them based on the following correlation aspects: (a) external correlation, which measures the similarity of the YMAL query to the original query and (b) internal correlation, which measures the capability of the YMAL query keywords in producing meaningful results with respect to the data source. Due to the complexity of generating YMAL queries, we propose a novel A* search-based technique to generate top-l YMAL queries efficiently. We also present a greedy-based approximation for it to improve the performance further. Extensive experiments verify the effectiveness and efficiency of our approach.
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    Journal Title
    World Wide Web
    DOI
    https://doi.org/10.1007/s11280-018-0630-x
    Copyright Statement
    © 2018 Springer Netherlands. This is an electronic version of an article published in World Wide Web, pp 1–24, 2018. 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
    Data structures and algorithms
    Distributed computing and systems software
    Information systems
    Database systems
    Publication URI
    http://hdl.handle.net/10072/380249
    Collection
    • Journal articles

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