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Novel Node Importance Measures to Improve Keyword Search over RDF Graphs

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Book cover Database and Expert Systems Applications (DEXA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11707))

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Abstract

A key contributor to the success of keyword search systems is a ranking mechanism that considers the importance of the retrieved documents. The notion of importance in graphs is typically computed using centrality measures that highly depend on the degree of the nodes, such as PageRank. However, in RDF graphs, the notion of importance is not necessarily related to the node degree. Therefore, this paper addresses two problems: (1) how to define importance measures in RDF graphs; (2) how to use these measures to help compile and rank results of keyword queries over RDF graphs. To solve these problems, the paper proposes a novel family of measures, called InfoRank, and a keyword search system, called QUIRA, for RDF graphs. Finally, this paper concludes with experiments showing that the proposed solution improves the quality of results in two keyword search benchmarks.

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Notes

  1. 1.

    http://dbpedia.org/sparql.

  2. 2.

    http://dblp.uni-trier.de.

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Acknowledgments

This work was partly funded by CAPES under grant 88881.134081/2016-01, by CNPq under grants 153908/2015-7, 302303/2017-0 and by FAPERJ under grant E-26-202.818/2017.

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Authors and Affiliations

  1. Department of Informatics, PUC-Rio, Rio de Janeiro, RJ, Brazil

    Elisa S. Menendez & Marco A. Casanova

  2. Computer Science Institute, UFF, Niteroi, RJ, Brazil

    Luiz A. P. Paes Leme

  3. Institut de Recherche en Informatique de Toulouse, IRIT, Toulouse, France

    Elisa S. Menendez & Mohand Boughanem

Authors
  1. Elisa S. Menendez
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  2. Marco A. Casanova
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  3. Luiz A. P. Paes Leme
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  4. Mohand Boughanem
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Corresponding author

Correspondence to Marco A. Casanova .

Editor information

Editors and Affiliations

  1. Clausthal University of Technology, Clausthal-Zellerfeld, Germany

    Sven Hartmann

  2. Johannes Kepler University of Linz, Linz, Austria

    Josef Küng

  3. The University of Texas at Arlington, Arlington, TX, USA

    Sharma Chakravarthy

  4. Johannes Kepler University of Linz, Linz, Austria

    Gabriele Anderst-Kotsis

  5. Software Competence Center Hagenberg, Hagenberg im Mühlkreis, Austria

    A Min Tjoa

  6. Johannes Kepler University of Linz, Linz, Austria

    Ismail Khalil

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Menendez, E.S., Casanova, M.A., Paes Leme, L.A.P., Boughanem, M. (2019). Novel Node Importance Measures to Improve Keyword Search over RDF Graphs. In: Hartmann, S., Küng, J., Chakravarthy, S., Anderst-Kotsis, G., Tjoa, A., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2019. Lecture Notes in Computer Science(), vol 11707. Springer, Cham. https://doi.org/10.1007/978-3-030-27618-8_11

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  • DOI: https://doi.org/10.1007/978-3-030-27618-8_11

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