KGGen: Extracting Knowledge Graphs from Plain Text with Language Models

• URL: http://arxiv.org/abs/2502.09956v1
• Date: Fri, 14 Feb 2025 07:28:08 GMT
• Title: KGGen: Extracting Knowledge Graphs from Plain Text with Language Models
• Authors: Belinda Mo, Kyssen Yu, Joshua Kazdan, Proud Mpala, Lisa Yu, Chris Cundy, Charilaos Kanatsoulis, Sanmi Koyejo,
• Abstract summary: We present a text-to-KG generator (KGGen) that clusters related entities to reduce sparsity in extracted KGs. KGGen is available as a Python library (textttpip install kg-gen), making it accessible to everyone. Along with KGGen, we release the first benchmark, Measure of Information in Nodes and Edges (MINE), that tests an extractor's ability to produce a useful KG from plain text.
• Score: 12.937091556995039
• License:
• Abstract: Recent interest in building foundation models for KGs has highlighted a fundamental challenge: knowledge-graph data is relatively scarce. The best-known KGs are primarily human-labeled, created by pattern-matching, or extracted using early NLP techniques. While human-generated KGs are in short supply, automatically extracted KGs are of questionable quality. We present a solution to this data scarcity problem in the form of a text-to-KG generator (KGGen), a package that uses language models to create high-quality graphs from plaintext. Unlike other KG extractors, KGGen clusters related entities to reduce sparsity in extracted KGs. KGGen is available as a Python library (\texttt{pip install kg-gen}), making it accessible to everyone. Along with KGGen, we release the first benchmark, Measure of of Information in Nodes and Edges (MINE), that tests an extractor's ability to produce a useful KG from plain text. We benchmark our new tool against existing extractors and demonstrate far superior performance.

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