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Anytime Large-Scale Analytics of Linked Open Data

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The Semantic Web – ISWC 2019 (ISWC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11778))

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Abstract

Analytical queries are queries with numerical aggregators: computing the average number of objects per property, identifying the most frequent subjects, etc. Such queries are essential to monitor the quality and the content of the Linked Open Data (LOD) cloud. Many analytical queries cannot be executed directly on the SPARQL endpoints, because the fair use policy cuts off expensive queries. In this paper, we show how to rewrite such queries into a set of queries that each satisfy the fair use policy. We then show how to execute these queries in such a way that the result provably converges to the exact query answer. Our algorithm is an anytime algorithm, meaning that it can give intermediate approximate results at any time point. Our experiments show that the approach converges rapidly towards the exact solution, and that it can compute even complex indicators at the scale of the LOD cloud.

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Notes

  1. 1.

    https://wiki.dbpedia.org/public-sparql-endpoint.

  2. 2.

    Given a set \(\varOmega \) with a probability distribution P, \(x \sim P( \varOmega )\) denotes that the element \(x \in \varOmega \) is drawn at random with a probability P(x).

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Acknowledgements

This work was partially supported by the grant ANR-16-CE23-0007-01 (“DICOS”).

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Authors and Affiliations

  1. Université de Tours, LIFAT, Blois, France

    Arnaud Soulet

  2. Telecom Paris, Institut Polytechnique de Paris, Paris, France

    Arnaud Soulet & Fabian M. Suchanek

Authors
  1. Arnaud Soulet
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  2. Fabian M. Suchanek
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Correspondence to Arnaud Soulet .

Editor information

Editors and Affiliations

  1. Fondazione Bruno Kessler, Trento, Italy

    Chiara Ghidini

  2. Linköping University, Linköping, Sweden

    Olaf Hartig

  3. University of Bonn, Bonn, Germany

    Maria Maleshkova

  4. University of Economics Prague, Prague, Czech Republic

    Vojtěch Svátek

  5. University of Illinois at Chicago, Chicago, IL, USA

    Isabel Cruz

  6. University of Chile, Santiago, Chile

    Aidan Hogan

  7. Memect Technology, Beijing, China

    Jie Song

  8. Mines Saint-Etienne, Saint-Etienne, France

    Maxime Lefrançois

  9. Inria Sophia Antipolis - Méditerranée, Sophia Antipolis, France

    Fabien Gandon

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Soulet, A., Suchanek, F.M. (2019). Anytime Large-Scale Analytics of Linked Open Data. In: Ghidini, C., et al. The Semantic Web – ISWC 2019. ISWC 2019. Lecture Notes in Computer Science(), vol 11778. Springer, Cham. https://doi.org/10.1007/978-3-030-30793-6_33

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  • DOI: https://doi.org/10.1007/978-3-030-30793-6_33

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  • Print ISBN: 978-3-030-30792-9

  • Online ISBN: 978-3-030-30793-6

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