KGGen: Extracting Knowledge Graphs from Plain Text with Language Models

• DOI: 10.48550/arxiv.2502.09956
• Publication Date: 2025-01-01
• Authors: Mo, Belinda; Yu, Kyssen; Kazdan, Joshua; Mpala, Proud; Yu, Lisa; Cundy, Chris; Kanatsoulis, Char...; Koyejo, Sanmi
• Keywords: FOS: Computer and information sciences; Computer Science - Machine Learning; Computer Science - Computation and Language; Artificial Intelligence (cs.AI); Computer Science - Artificial Intelligence; Computation and Language (cs.CL); Information Retrieval (cs.IR); Computer Science - Information Retrieval; Machine Learning (cs.LG)

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.