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

• URL: http://arxiv.org/abs/2309.03685v2
• Date: Tue, 5 Mar 2024 21:56:43 GMT
• Title: PyGraft: Configurable Generation of Synthetic Schemas and Knowledge Graphs at Your Fingertips
• Authors: Nicolas Hubert, Pierre Monnin, Mathieu d'Aquin, Davy Monticolo, Armelle Brun
• Abstract summary: PyGraft is a Python-based tool that generates customized, domain-agnostic schemas and KGs. We aim to empower the generation of a more diverse array of KGs for benchmarking novel approaches in areas such as graph-based machine learning (ML) In ML, this should foster a more holistic evaluation of model performance and generalization capability, thereby going beyond the limited collection of available benchmarks.
• Score: 3.5923669681271257
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge graphs (KGs) have emerged as a prominent data representation and management paradigm. Being usually underpinned by a schema (e.g., an ontology), KGs capture not only factual information but also contextual knowledge. In some tasks, a few KGs established themselves as standard benchmarks. However, recent works outline that relying on a limited collection of datasets is not sufficient to assess the generalization capability of an approach. In some data-sensitive fields such as education or medicine, access to public datasets is even more limited. To remedy the aforementioned issues, we release PyGraft, a Python-based tool that generates highly customized, domain-agnostic schemas and KGs. The synthesized schemas encompass various RDFS and OWL constructs, while the synthesized KGs emulate the characteristics and scale of real-world KGs. Logical consistency of the generated resources is ultimately ensured by running a description logic (DL) reasoner. By providing a way of generating both a schema and KG in a single pipeline, PyGraft's aim is to empower the generation of a more diverse array of KGs for benchmarking novel approaches in areas such as graph-based machine learning (ML), or more generally KG processing. In graph-based ML in particular, this should foster a more holistic evaluation of model performance and generalization capability, thereby going beyond the limited collection of available benchmarks. PyGraft is available at: https://github.com/nicolas-hbt/pygraft.

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