HKUST-KnowComp/AutoSchemaKG
AutoSchemaKG is a two-stage framework that uses LLMs to extract knowledge graph triples from text and automatically generate schemas via conceptualization.

The framework operates in two stages: first extracting entities and events as triples from unstructured text using LLMs, then inducing schemas through conceptualization to enable semantic bridges across domains. It includes an atlas-rag package for knowledge graph hosting and multi-hop QA evaluation, and supports multilingual processing, batch generation, and PDF/Markdown conversion. The project achieves state-of-the-art performance on multiple benchmarks.