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 knowledge graphs. 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: this https URL

PyGraft 是一个 Python 工具，用于生成高度定制的、领域无关的模式和知识图谱，以解决在教育或医学等数据敏感领域中公共数据集不足的问题，并促进基于图的机器学习等领域中模型性能和泛化能力的综合评估。