Author:

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Flanders Make at KU Leuven, Leuven.AI

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.