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Communication Dans Un Congrès Année : 2024

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

Résumé

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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hal-04491258 , version 1 (05-03-2024)

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Nicolas Hubert, Pierre Monnin, Mathieu D’aquin, Davy Monticolo, Armelle Brun. PyGraft: Configurable Generation of Synthetic Schemas and Knowledge Graphs at Your Fingertips. ESWC 2024 - 21st International Conference on Semantic Web, May 2024, Hersonissos, Greece. ⟨10.5281/zenodo.10243209⟩. ⟨hal-04491258⟩
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