KGGen is an open source tool developed by the Stanford Trusted Artificial Intelligence Research Laboratory (STAIR Lab) for automatic knowledge graph generation from arbitrary text. Its core features include:
- Text-to-Knowledge Graph Conversion: Extracting entities (e.g., names of people, places, concepts, etc.) and the relationships between them from unstructured text through natural language processing techniques to build structured knowledge networks.
- Multilingual Model Integration: Support mainstream pre-trained language models (e.g., BERT, GPT, etc.) to enhance comprehension of texts from different domains.
- Clustering Optimization: Advanced clustering algorithms are used to improve the connectivity and logic of the graph and avoid fragmented relationships.
- Open Source Customizable: A full Python codebase is provided, allowing users to modify parameters or extend functionality to meet specific needs.
- Data export: Support JSON and other formats to export the generated knowledge graph for subsequent analysis or integration with other tools.
KGGen is particularly suitable for researchers and developers to quickly realize knowledge extraction tasks, and its latest version was released on February 20, 2025, hosted on the GitHub open source platform.
This answer comes from the articleKG Gen: an open source tool for automatic knowledge graph generation from plain textThe
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