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Extending SPARQL with Similarity Joins

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The Semantic Web – ISWC 2020 (ISWC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12506))

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Abstract

We propose techniques that support the efficient computation of multidimensional similarity joins in an RDF/SPARQL setting, where similarity in an RDF graph is measured with respect to a set of attributes selected in the SPARQL query. While similarity joins have been studied in other contexts, RDF graphs present unique challenges. We discuss how a similarity join operator can be included in the SPARQL language, and investigate ways in which it can be implemented and optimised. We devise experiments to compare three similarity join algorithms over two datasets. Our results reveal that our techniques outperform DBSimJoin: a PostgreSQL extension that supports similarity joins.

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Notes

  1. 1.

    We use prefixes as defined in http://prefix.cc.

  2. 2.

    http://jena.apache.org.

  3. 3.

    https://javacc.github.io/javacc/.

  4. 4.

    We use the library provided by Chambers at https://github.com/jchambers/jvptree.

  5. 5.

    We use the Java implementation provided by Stavrev at https://gitlab.com/jadro-ai-public/flann-java-port.git.

  6. 6.

    We use a truthy dump available in February, 2020.

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Acknowledgements

This work was partly funded by the Millennium Institute for Foundational Research on Data, by FONDECYT Grant No. 1181896 and by CONICYT Grant No. 21170616. We thank the reviewers for their feedback.

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Authors and Affiliations

  1. Department of Computer Science, Universidad de Chile, Millenium Institute for Foundational Research on Data, Santiago, Chile

    Sebastián Ferrada, Benjamin Bustos & Aidan Hogan

Authors
  1. Sebastián Ferrada
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  2. Benjamin Bustos
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  3. Aidan Hogan
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Corresponding author

Correspondence to Sebastián Ferrada .

Editor information

Editors and Affiliations

  1. University of Edinburgh, Edinburgh, UK

    Jeff Z. Pan

  2. University of Liverpool, Liverpool, UK

    Valentina Tamma

  3. University of Bari, Bari, Italy

    Claudia d’Amato

  4. University of California, Santa Barbara, Santa Barbara, CA, USA

    Krzysztof Janowicz

  5. California State University, Long Beach, Long Beach, CA, USA

    Bo Fu

  6. Vienna University of Economics and Business, Vienna, Austria

    Axel Polleres

  7. Rensselaer Polytechnic Institute, Troy, NY, USA

    Oshani Seneviratne

  8. Massachusetts Institute of Technology, Cambridge, MA, USA

    Lalana Kagal

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Ferrada, S., Bustos, B., Hogan, A. (2020). Extending SPARQL with Similarity Joins. In: Pan, J.Z., et al. The Semantic Web – ISWC 2020. ISWC 2020. Lecture Notes in Computer Science(), vol 12506. Springer, Cham. https://doi.org/10.1007/978-3-030-62419-4_12

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  • DOI: https://doi.org/10.1007/978-3-030-62419-4_12

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  • Print ISBN: 978-3-030-62418-7

  • Online ISBN: 978-3-030-62419-4

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