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Knowledge graphs (KGs) have emerged as a prominent data representation andmanagement paradigm. Being usually underpinned by a schema (e.g. an ontology),KGs capture not only factual information but also contextual knowledge. In sometasks, a few KGs established themselves as standard benchmarks. However, recentworks outline that relying on a limited collection of datasets is notsufficient to assess the generalization capability of an approach. In somedata-sensitive fields such as education or medicine, access to public datasetsis even more limited. To remedy the aforementioned issues, we release PyGraft,a Python-based tool that generates highly customized, domain-agnostic schemasand knowledge graphs. The synthesized schemas encompass various RDFS and OWLconstructs, while the synthesized KGs emulate the characteristics and scale ofreal-world KGs. Logical consistency of the generated resources is ultimatelyensured by running a description logic (DL) reasoner. By providing a way ofgenerating both a schema and KG in a single pipeline, PyGraft's aim is toempower the generation of a more diverse array of KGs for benchmarking novelapproaches in areas such as graph-based machine learning (ML), or moregenerally KG processing. In graph-based ML in particular, this should foster amore holistic evaluation of model performance and generalization capability,thereby going beyond the limited collection of available benchmarks. PyGraft isavailable at: https://github.com/nicolas-hbt/pygraft.

PyGraft: Configurable Generation of Schemas and Knowledge Graphs at Your Fingertips