#shacl

In the past year or so, our research team designed, developed and published RDFGraphGen, a general-purpose, domain-independent generator of synthetic RDF knowledge graphs, based on SHACL constraints. Today, we published a preprint detailing its design and implementation: "RDFGraphGen: A Synthetic RDF Graph Generator based on SHACL Constraints".

So, how does RDFGraphGen work, and why was it needed?

The Shapes Constraint Language (SHACL) is a W3C standard which specifies ways to validate data in RDF graphs, by defining constraining shapes. However, even though the main purpose of SHACL is validation of existing RDF data, in order to solve the problem with the lack of available RDF datasets in multiple RDF-based application development processes, we envisioned and implemented a reverse role for SHACL: we use SHACL shape definitions as a starting point to generate synthetic data for an RDF graph. The generation process involves extracting the constraints from the SHACL shapes, converting the specified constraints into rules, and then generating artificial data for a predefined number of RDF entities, based on these rules. The purpose of RDFGraphGen is the generation of small, medium or large RDF knowledge graphs for the purpose of benchmarking, testing, quality control, training and other similar purposes for applications from the RDF, Linked Data and Semantic Web domain.

RDFGraphGen is open-source and is available as a ready-to-use Python package.

Preprint: https://arxiv.org/abs/2407.17941 Authors: Marija Vecovska and Milos Jovanovik RDFGraphGen on GitHub: https://github.com/mveco/RDFGraphGen RDFGraphGen on PyPi: https://pypi.org/project/rdf-graph-gen/