An Algorithm Based on Grammatical Evolution for Discovering SHACL Constraints

The continuous evolution of heterogeneous RDF data has led to an increase of inconsistencies on the Web of data (i.e. missing data and errors) that we assume to be inherent to RDF data graphs. To improve their quality, the W3C recommendation SHACL allows to express various constraints that RDF data must conform to and detect nodes violating them. However, acquiring representative and meaningful SHACL constraints from complex and very large RDF data graphs is very challenging and tedious. Consequently, several recent works focus on the automatic generation of these constraints. We propose an approach based on grammatical evolution (GE) for extracting representative SHACL constraints by mining an RDF data graph. This approach uses a probabilistic SHACL validation framework to consider the inherent errors in RDF data. The results highlight the relevance of this approach in discovering SHACL shapes inspired by association rule patterns from a real-world RDF data graph.