HKUST-KnowComp/AutoSchemaKG
This repository contains the implementation of AutoSchemaKG, a novel framework for automatic knowledge graph construction that combines schema generation via conceptualization.
Employs a two-stage LLM-based pipeline that extracts entity/event triples from unstructured text, then automatically induces schemas through conceptualization to enable zero-shot cross-domain inference. Supports batch processing with configurable LLM backends (OpenAI, Hugging Face), vector storage integration, and Neo4j hosting for large-scale graphs. Pre-built ATLAS knowledge graphs contain 900M+ nodes across Wikipedia, academic papers, and web content, with evaluation frameworks for factuality (FELM) and general performance (MMLU).
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Language
Python
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MIT
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Last pushed
Jan 14, 2026
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