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A Framework to Include and Exploit Probabilistic Information in SHACL Validation Reports

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The Semantic Web (ESWC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13870))

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Abstract

The Shapes Constraint Language (SHACL) is a W3C recommendation which allows to represent constraints in RDF– shape graphs –, and validate RDF data graphs against these constraints. A SHACL validator produces a validation report whose result is false for a shape graph as soon as there is at least one node in the RDF data graph that does not conform to the shape. This Boolean result of the validation of an RDF data graph against an RDF shape graph is not suitable for discovering new high-potential shapes from the RDF data. In this paper, we propose a probabilistic framework to accept shapes with a realistic proportion of nodes in an RDF data graph that does not conform to it. Based on this framework, we propose an extension of the SHACL validation report to express a set of metrics including the generality and likelihood of shapes and we define a method to test a shape as a hypothesis test. Finally, we present the results of experiments conducted to validate a test RDF data graph against a set of shapes.

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Notes

  1. 1.

    prefix psh: <http://ns.inria.fr/probabilistic-shacl/>.

  2. 2.

    https://github.com/Wimmics/CovidOnTheWeb.

  3. 3.

    https://github.com/RemiFELIN/RDFMining/tree/eswc_2023.

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Acknowledgements

This work has been partially founded by the 3IA Côte d’Azur “Investments in the Future” project managed by the National Research Agency (ANR) with the reference number ANR-19-P3IA-0002.

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Authors and Affiliations

  1. Université Côte d’Azur, Inria, I3S, Sophia-Antipolis, France

    Rémi Felin, Catherine Faron & Andrea G. B. Tettamanzi

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  1. Rémi Felin
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  2. Catherine Faron
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  3. Andrea G. B. Tettamanzi
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Corresponding author

Correspondence to Rémi Felin .

Editor information

Editors and Affiliations

  1. Universidade de Lisboa, Lisbon, Portugal

    Catia Pesquita

  2. University of London, London, UK

    Ernesto Jimenez-Ruiz

  3. Rensselaer Polytechnic Institute, Troy, MI, USA

    Jamie McCusker

  4. Universidade de Lisboa, Lisbon, Portugal

    Daniel Faria

  5. Fondazione Bruno Kessler, Povo, Trento, Italy

    Mauro Dragoni

  6. KU Leuven, Sint-Katelijne-Waver, Belgium

    Anastasia Dimou

  7. EURECOM, Biot, France

    Raphael Troncy

  8. University of Mannheim, Mannheim, Germany

    Sven Hertling

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Felin, R., Faron, C., Tettamanzi, A.G.B. (2023). A Framework to Include and Exploit Probabilistic Information in SHACL Validation Reports. In: Pesquita, C., et al. The Semantic Web. ESWC 2023. Lecture Notes in Computer Science, vol 13870. Springer, Cham. https://doi.org/10.1007/978-3-031-33455-9_6

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  • DOI: https://doi.org/10.1007/978-3-031-33455-9_6

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