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XPloreRank: exploring XML data via you may also like queries

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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 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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Acknowledgments

This work is supported by the Australian Research Council Discovery Grants DP140103499, DPDP170104747, and DP180100212.

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Authors and Affiliations

  1. Swinburne University of Technology, Melbourne, Australia

    Mehdi Naseriparsa, Chengfei Liu & Rui Zhou

  2. Griffith University, Gold Coast, Australia

    Md. Saiful Islam

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  1. Mehdi Naseriparsa
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  2. Chengfei Liu
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  3. Md. Saiful Islam
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  4. Rui Zhou
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Correspondence to Mehdi Naseriparsa.

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Naseriparsa, M., Liu, C., Islam, M.S. et al. XPloreRank: exploring XML data via you may also like queries. World Wide Web 22, 1727–1750 (2019). https://doi.org/10.1007/s11280-018-0630-x

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