PyGraft: Configurable Generation of Synthetic Schemas and Knowledge Graphs at Your Fingertips (2309.03685v2)

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 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.

• The paper introduces PyGraft, a tool that generates both schemas and knowledge graphs based on user-defined parameters and Semantic Web standards.
• It employs comprehensive relation properties and pre-reasoning checks to ensure logical consistency and semantic richness in the generated datasets.
• Experimental evaluations demonstrate PyGraft's scalability and robustness, making it valuable for synthetic data generation in ML and neuro-symbolic AI research.

Overview of PyGraft: A Synthetic Knowledge Graph Generator

The paper presents "PyGraft," a Python-based tool designed for the generation of synthetic schemas and knowledge graphs (KGs). This software aims at addressing the limitations of existing benchmark datasets used in graph-based ML and KG processing tasks by offering a customizable, domain-agnostic alternative.

Key Contributions and Methodology

PyGraft distinguishes itself by integrating both schema and KG generation within a single pipeline and providing fine-grained configurability through a rich set of user-specified parameters. It supports an array of RDFS and OWL constructs, ensuring its generated outputs are logically consistent with Semantic Web standards.

1. Schema Generation: The tool creates a hierarchical class structure with inheritance and disjointness properties. The schema generation process iterates through a specified set of classes, ensuring compliance with user-defined depth and proportions of inheritance and disjointness.
2. Relation Properties: The paper highlights a comprehensive set of relation properties supported by PyGraft, including reflexive, transitive, symmetric, and inverse properties. These attributes allow for the creation of complex and semantically rich datasets.
3. KG Generation: Entities and relationships in the KG are instantiated based on the generated schema. This ensures the fidelity and semantic consistency of the graph. The authors implement pre-reasoning checks to preempt potential inconsistencies before the deployment of a description logic reasoner.

Experimental Evaluation

The authors conduct scalability tests demonstrating that PyGraft is capable of efficiently generating large KGs with a significant number of entities and triples. The software's robustness is evidenced through consistent outputs even with complex configurations involving numerous schema constraints.

Implications and Future Prospects

Practical Impact: PyGraft offers utility in areas with restricted access to public datasets, such as medicine and education, by enabling the development and testing of ML models on synthetic yet representative datasets. This capability is crucial for testing the generalization abilities of models on diverse and complex distributions.

Theoretical Significance: The dual-generation of schema and KG facilitates the exploration of schema-aware learning frameworks. PyGraft can advance research in neuro-symbolic AI by providing datasets that integrate symbolic information in KG embeddings or learning tasks.

Future Developments: Enhancements proposed by the authors include improving large KG serialization, consistency verification mechanisms, and extending the tool's capability with features like literal generation. These improvements would further solidify PyGraft as a foundational tool for both KG-based research and practical applications.

Conclusion

PyGraft successfully fills a critical gap by providing a tool for synthetic data generation supporting a breadth of logical constructs in the context of KGs and schemas. Its release paves the way for more robust evaluations of KG-based models and potentially fuels innovation in semantic technology applications across various domains. The community-driven approach in developing PyGraft augurs well for its sustainability and adaptability in an evolving field.