A Simplex Approach to Synthetic Knowledge Graph Generation
Ana Alexandra Morim da Silva, Axel-Cyrille Ngonga Ngomo, Atul Bhopalsing Pundir, Michael Röder
Problems Identified (4)
Scalable KG Benchmarking: The growing scale of knowledge graphs requires scalable processing systems and large knowledge graphs for accurate benchmarking.
Missing Higher-Order Structures: Current data-driven synthetic knowledge graph generators operate at the triple level and do not consider higher-order graph structures.
Scalable KG Benchmarking: The growing scale of knowledge graphs requires scalable processing systems and large knowledge graphs for accurate benchmarking.
Missing Higher-Order Structures: Current data-driven synthetic knowledge graph generators operate at the triple level and do not consider higher-order graph structures.
Proposed Solutions (4)
SimplexKG: SimplexKG is a simplex-based synthetic knowledge graph generator that analyzes d-dimensional simplices in input knowledge graphs and uses the resulting simplicial networks to generate synthetic graphs of arbitrary size.
Higher-Dimensional Structure Modeling: The approach leverages higher-dimensional structures to improve the realism of generated synthetic knowledge graphs.
SimplexKG: SimplexKG is a simplex-based synthetic knowledge graph generator that analyzes d-dimensional simplices in input knowledge graphs and uses the resulting simplicial networks to generate synthetic graphs of arbitrary size.
Higher-Dimensional Structure Modeling: The approach leverages higher-dimensional structures to improve the realism of generated synthetic knowledge graphs.
Results (3)
Improved Structural Fidelity:
Improved Triple Store Benchmarking:
Improved Structural Fidelity:
Research Domain
synthetic knowledge graph generation and benchmarking