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SHACL Constraints with Inference Rules

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Book cover 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

The Shapes Constraint Language (SHACL) has been recently introduced as a W3C recommendation to define constraints that can be validated against RDF graphs. Interactions of SHACL with other Semantic Web technologies, such as ontologies or reasoners, is a matter of ongoing research. In this paper we study the interaction of a subset of SHACL with inference rules expressed in datalog. On the one hand, SHACL constraints can be used to define a “schema” for graph datasets. On the other hand, inference rules can lead to the discovery of new facts that do not match the original schema. Given a set of SHACL constraints and a set of datalog rules, we present a method to detect which constraints could be violated by the application of the inference rules on some graph instance of the schema, and update the original schema, i.e, the set of SHACL constraints, in order to capture the new facts that can be inferred. We provide theoretical and experimental results of the various components of our approach.

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Notes

  1. 1.

    https://github.com/paolo7/ISWC2019-appendix/raw/master/Appendix.pdf.

  2. 2.

    https://github.com/paolo7/ISWC2019-code.

  3. 3.

    For this step we implement a version of the Chase algorithm [4].

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Acknowledgments

This work was supported by an Institutional Links grant, ID 333778, under the Newton-Katip Çelebi Fund. The grant is funded by the UK Department for Business, Energy and Industrial Strategy and the Scientific and Technological Research Council of Turkey (TUBITAK) under grant 116E918, and delivered by the British Council.

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Correspondence to Paolo Pareti .

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Pareti, P., Konstantinidis, G., Norman, T.J., Şensoy, M. (2019). SHACL Constraints with Inference Rules. 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_31

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  • DOI: https://doi.org/10.1007/978-3-030-30793-6_31

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