Abstract
The success of the Semantic Web highly depends on its ingredients. If we want to fully realize the vision of a machine-readable Web, it is crucial that Linked Data are actually useful for machines consuming them. On this background it is not surprising that (Linked) Data validation is an ongoing research topic in the community. However, most approaches so far either do not consider reasoning, and thereby miss the chance of detecting implicit constraint violations, or they base themselves on a combination of different formalisms, e.g. Description Logics combined with SPARQL. In this paper, we propose using Rule-Based Web Logics for RDF validation focusing on the concepts needed to support the most common validation constraints, such as Scoped Negation As Failure (SNAF), and the predicates defined in the Rule Interchange Format (RIF). We prove the feasibility of the approach by providing an implementation in Notation3 Logic. As such, we show that rule logic can cover both validation and reasoning if it is expressive enough.
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Notes
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See, e.g. statistics at: http://lod-cloud.net/.
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This could be expressed by an extended version of MATCH as for example the constraint “Negative Literal Pattern Matching” in [12].
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More about that in Sect. 5.1.
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As explained in Sect. 4.4 there are alternative ways to express the predicate log:notEqualTo in
, the antecedence of the entire rule could also be expressed only using RIF predicates.
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Acknowledgements
The described research activities were funded by Ghent University, imec, Flanders Innovation & Entrepreneurship (AIO), the Fund for Scientific Research Flanders (FWO Flanders), and the European Union, in the context of the project “DiSSeCt”, which is a collaboration by SMIT, DistriNet, and IDLab.
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Arndt, D., Meester, B.D., Dimou, A., Verborgh, R., Mannens, E. (2017). Using Rule-Based Reasoning for RDF Validation. In: Costantini, S., Franconi, E., Van Woensel, W., Kontchakov, R., Sadri, F., Roman, D. (eds) Rules and Reasoning. RuleML+RR 2017. Lecture Notes in Computer Science(), vol 10364. Springer, Cham. https://doi.org/10.1007/978-3-319-61252-2_3
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