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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.

References

  1. Bogaerts, B., Jakubowski, M., den Bussche, J.V.: Expressiveness of SHACL features. In: ICDT (2022)

    Google Scholar 

  2. Bogaerts, B., Jakubowski, M., Van den Bussche, J.: SHACL: a description logic in disguise (2021)

    Google Scholar 

  3. Boneva, I., Labra Gayo, J.E., Prud’hommeaux, E.G.: Semantics and validation of shapes schemas for RDF. In: d’Amato, C., Fernandez, M., Tamma, V., Lecue, F., Cudré-Mauroux, P., Sequeda, J., Lange, C., Heflin, J. (eds.) ISWC 2017. LNCS, vol. 10587, pp. 104–120. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68288-4_7

    Chapter  Google Scholar 

  4. Cadorel, L., Tettamanzi, A.: Mining RDF data of COVID-19 scientific literature for interesting association rules. In: 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), pp. 145–152 (2020)

    Google Scholar 

  5. Cimmino, A., Fernández-Izquierdo, A., García-Castro, R.: Astrea: automatic generation of SHACL shapes from ontologies. In: Harth, A., et al. (eds.) ESWC 2020. LNCS, vol. 12123, pp. 497–513. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-49461-2_29

    Chapter  Google Scholar 

  6. Corby, O., Faron Zucker, C.: STTL: a SPARQL-based transformation Language for RDF. In: 11th International Conference on Web Information Systems and Technologies. Lisbon, Portugal (2015)

    Google Scholar 

  7. Corman, J., Florenzano, F., Reutter, J.L., Savković, O.: Validating Shacl constraints over a Sparql endpoint. In: Ghidini, C., et al. (eds.) ISWC 2019. LNCS, vol. 11778, pp. 145–163. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30793-6_9

    Chapter  Google Scholar 

  8. Corman, J., Reutter, J.L., Savković, O.: Semantics and validation of recursive SHACL. In: Vrandečić, D., et al. (eds.) ISWC 2018. LNCS, vol. 11136, pp. 318–336. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00671-6_19

    Chapter  Google Scholar 

  9. Debruyne, C., McGlinn, K.: Reusable SHACL constraint components for validating geospatial linked data (short paper). In: GeoLD@ESWC (2021)

    Google Scholar 

  10. Fernandez-Álvarez, D., Labra-Gayo, J.E., Gayo-Avello, D.: Automatic extraction of shapes using sheXer. Knowl.-Based Syst. 238, 107975 (2022). https://doi.org/10.1016/j.knosys.2021.107975, https://www.sciencedirect.com/science/article/pii/S0950705121010972

  11. Figuera, M., Rohde, P.D., Vidal, M.E.: Trav-SHACL: efficiently validating networks of SHACL constraints. In: Proceedings of the Web Conference 2021, pp. 3337–3348. WWW 2021, Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3442381.3449877

  12. K Soman, R.: Modelling construction scheduling constraints using shapes constraint language (SHACL), pp. 351–358 (2019). https://doi.org/10.35490/EC3.2019.170

  13. Kontokostas, D., Knublauch, H.: Shapes constraint language (SHACL). In: W3C Recommendation, W3C (2017). https://www.w3.org/TR/2017/REC-shacl-20170720/

  14. Köcher, A., Vieira da Silva, L.M., Fay, A.: Constraint checking of skills using SHACL (2021)

    Google Scholar 

  15. Leinberger, M., Seifer, P., Rienstra, T., Lämmel, R., Staab, S.: Deciding SHACL shape containment through description logics reasoning. In: Pan, J.Z., et al. (eds.) ISWC 2020. LNCS, vol. 12506, pp. 366–383. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-62419-4_21

    Chapter  Google Scholar 

  16. Michel, F., et al.: Covid-on-the-web: knowledge graph and services to advance COVID-19 research. In: Pan, J.Z., et al. (eds.) ISWC 2020. LNCS, vol. 12507, pp. 294–310. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-62466-8_19

    Chapter  Google Scholar 

  17. Mihindukulasooriya, N., Rashid, M.R.A., Rizzo, G., García-Castro, R., Corcho, O., Torchiano, M.: RDF shape induction using knowledge base profiling, pp. 1952–1959. SAC 2018, Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3167132.3167341

  18. Pandit, H., O’Sullivan, D., Lewis, D.: Using ontology design patterns to define SHACL shapes. In: WOP@ISWC, pp. 67–71. Monterey California, USA (2018)

    Google Scholar 

  19. Pareti, P., Konstantinidis, G.: A review of SHACL: from data validation to schema reasoning for RDF graphs. In: Šimkus, M., Varzinczak, I. (eds.) Reasoning Web. Declarative Artificial Intelligence. Reasoning Web 2021. Lecture Notes in Computer Science, vol. 13100, pp. 115–144. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-95481-9_6

    Chapter  Google Scholar 

  20. Pareti, P., Konstantinidis, G., Norman, T.J., Şensoy, M.: SHACL constraints with inference rules. In: Ghidini, C., et al. (eds.) ISWC 2019. LNCS, vol. 11778, pp. 539–557. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30793-6_31

    Chapter  Google Scholar 

  21. Alva Principe, R.A., Maurino, A., Palmonari, M., Ciavotta, M., Spahiu, B.: ABSTAT-HD: a scalable tool for profiling very large knowledge graphs. VLDB J., 1–26 (2021). https://doi.org/10.1007/s00778-021-00704-2

  22. Rabbani, K., Lissandrini, M., Hose, K.: SHACL and ShEx in the wild: a community survey on validating shapes generation and adoption. In: Companion Proceedings of the Web Conference 2022, pp. 260–263. WWW 2022, Association for Computing Machinery, New York, NY, USA (2022). https://doi.org/10.1145/3487553.3524253

  23. Wright, J., Rodríguez Méndez, S.J., Haller, A., Taylor, K., Omran, P.G.: Schímatos: a SHACL-based web-form generator for knowledge graph editing. In: Pan, J.Z., et al. (eds.) ISWC 2020. LNCS, vol. 12507, pp. 65–80. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-62466-8_5

    Chapter  Google Scholar 

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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

Authors
  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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