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Mining Significant Maximum Cardinalities in Knowledge Bases

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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

Semantic Web connects huge knowledge bases whose content has been generated from collaborative platforms and by integration of heterogeneous databases. Naturally, these knowledge bases are incomplete and contain erroneous data. Knowing their data quality is an essential long-term goal to guarantee that querying them returns reliable results. Having cardinality constraints for roles would be an important advance to distinguish correctly and completely described individuals from those having data either incorrect or insufficiently informed. In this paper, we propose a method for automatically discovering from the knowledge base’s content the maximum cardinality of roles for each concept, when it exists. This method is robust thanks to the use of Hoeffding’s inequality. We also design an algorithm, named C3M, for an exhaustive search of such constraints in a knowledge base benefiting from pruning properties that drastically reduce the search space. Experiments conducted on DBpedia demonstrate the scaling up of C3M, and also highlight the robustness of our method, with a precision higher than 95%.

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Notes

  1. 1.

    We use the Description Logics (DL) [2] terminology, as DL are the theoretical foundations of OWL, so we use the terms concept (i.e. class), role (i.e. property), individual and fact (i.e. instances).

  2. 2.

    The prototype and the results are available at https://github.com/asoulet/c3m, both in CSV and in RDF (Turtle); we provide also the schema of our constraints expressed in RDF.

  3. 3.

    DL formal semantics are given in terms of interpretations, see [2].

  4. 4.

    We denote \(C \sqsubset C'\) when \(C \sqsubseteq C'\) and \(C' \not \sqsubseteq C\).

  5. 5.

    http://jena.apache.org and https://dbpedia.org.

  6. 6.

    The results for \(min_\tau = 0.97\) and the ground truth used to evaluate the precision are available at https://github.com/asoulet/c3m.

  7. 7.

    https://wiki.dbpedia.org/services-resources/datasets/dbpedia-datasets.

  8. 8.

    We do not compare our method with [15] because in the case of DBpedia, this method systematically returns a wrong maximum cardinality for all constraints.

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Acknowledgements

This work was partially supported by the grant ANR-18-CE38-0009 (“SESAME”).

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Authors and Affiliations

  1. Université de Tours, LIFAT, Blois, France

    Arnaud Giacometti, Béatrice Markhoff & Arnaud Soulet

Authors
  1. Arnaud Giacometti
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  2. Béatrice Markhoff
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  3. Arnaud Soulet
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Correspondence to Arnaud Soulet .

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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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Giacometti, A., Markhoff, B., Soulet, A. (2019). Mining Significant Maximum Cardinalities in Knowledge Bases. 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_11

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  • DOI: https://doi.org/10.1007/978-3-030-30793-6_11

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