KGGen: Extracting Knowledge Graphs from Plain Text with Language Models

This paper introduces KGGen, a Python library that uses language models to extract high-quality knowledge graphs from plain text, addressing the data scarcity problem in knowledge graph research where human-labelled graphs are scarce. A key differentiator is that KGGen clusters related entities to reduce sparsity in the resulting graphs, and the authors release MINE, the first benchmark for evaluating text-to-KG extraction quality.

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