Skip to content

Ontology2Graph

A Framework for Synthetic Knowledge Graph Generation using Large Language Models.

Abstract

Ontology2Graph is a comprehensive Python framework designed to generate synthetic Knowledge Graphs thanks to Large Language Model (LLM). A synthetic knowledge graph is a knowledge graph that mimics the structure and properties of real-world knowledge graphs. It is typically created for purposes such as testing, research, or training machine learning models, without relying on actual data. These graphs contain nodes (entities) and edges (relationships) that follow specific patterns or distributions, allowing users to study or develop algorithms in a controlled environment. The system provides a complete computational pipeline that transforms ontological schemas into semantically coherent knowledge graphs with integrated quality assurance mechanisms.

Overview

Knowledge Graphs have emerged as fundamental structures for representing complex relationships in semantic data. This framework addresses the challenge of generating synthetic knowledge graphs at scale by leveraging the reasoning capabilities of Large Language Models while ensuring adherence to ontological constraints and semantic consistency.

Ontology2Graph implements a modular architecture that supports:

  • Automated knowledge graph generation from ontological specifications
  • Comprehensive graph analysis and key performance indicator computation
  • Interactive visualization capabilities for structural analysis
  • Graph merging and consolidation algorithms
  • Quality control and validation mechanisms

Monitoring and Logging

The system generates comprehensive logs containing:

  • Generation timestamps and model identifiers
  • Token utilization metrics
  • Validation results and error reports
  • Performance indicators

Quality Assurance

  • Syntactic TTL validation using external tools
  • Semantic consistency checking against source ontologies
  • Structural analysis for graph coherence assessment
  • Automated error detection and quarantine mechanisms

Performance Considerations

  • LLM API rate limiting considerations
  • Parallel processing support for visualization rendering

Research Applications

This framework supports various research applications in:

  • Semantic data augmentation for machine learning datasets
  • Ontology validation and consistency testing
  • Knowledge graph structural analysis and comparison
  • Synthetic data generation for privacy-preserving research

Reference and Documentation