Abstract
Creating SHACL shapes for the validation of RDF graphs is a non-trivial endeavor. Automated shape extraction systems typically derive SHACL shapes from RDF graphs, and thus, their effectiveness is inherently influenced by the size and complexity of the RDF graph. However, these systems often overlook the constraints imposed by individual artifacts, although RDF graphs are often constructed by applying ontology terms to heterogeneous data. Only a few systems extract SHACL shapes from either the data schema or the ontology, leading, in either case, to limited or incomplete constraints. We propose SCOOP, a framework that exploits all artifacts associated with the construction of an RDF graph, i.e. data schemas, ontologies, and mapping rules, and integrates the SHACL shapes extracted from each artifact into a unified shapes graph. We applied our approach to real-world use cases and experimental results showed that SCOOP outperforms systems that extract SHACL shapes from RDF graphs, generating more than double the types of constraints than those systems, and effectively identifying missing and erroneous RDF triples during the validation process.
Resource type: Software Framework — License: Apache-2.0
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The mapping rules will be publicly available soon by ERA (era.europa.eu/).
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Acknowledgement
Xuemin Duan and Anastasia Dimou are partially supported by Flanders Make, the research centre for the manufacturing industry, and the Flanders innovation and entrepreneurship (VLAIO) through the KG3D project. David Chaves-Fraga is funded by the Galician Ministry of Education, University and Professional Training and the European Regional Development Fund (ERDF/FEDER program) through grants ED431C2018/29 and ED431G2019/04. The resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation - Flanders (FWO) and the Flemish Government.
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Duan, X., Chaves-Fraga, D., Derom, O., Dimou, A. (2024). SCOOP All the Constraints’ Flavours for Your Knowledge Graph. In: Meroño Peñuela, A., et al. The Semantic Web. ESWC 2024. Lecture Notes in Computer Science, vol 14665. Springer, Cham. https://doi.org/10.1007/978-3-031-60635-9_13
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