
PyGraft is an open-source Python library for generating synthetic yet realistic schemas and (KGs) based on user-specified parameters. The generated resources are domain-agnostic, i.e. they are not tied to a specific application field. Being able to synthesize schemas and KGs is an important milestone for conducting research in domains where data is sensitive or not readily available. PyGraft allows researchers and practitioners to generate schemas and KGs on the fly, provided minimal knowledge about the desired specifications. PyGraft has the following features: possibility to generate a schema, a KG, or both highly-tunable process based on a broad array of user-specified parameters schemas and KGs are built with an extended set of RDFS and OWL constructs logical consistency is ensured by the use of a DL reasoner (HermiT)
python, semantic-web, artificial-intelligence, knowledge-graph, benchmarking, ontology, linked-data, data-generator, rdf, owl, rdfs
python, semantic-web, artificial-intelligence, knowledge-graph, benchmarking, ontology, linked-data, data-generator, rdf, owl, rdfs
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 1 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
