KGGen: Extracting Knowledge Graphs from Plain Text with Language Models
This addresses the data scarcity problem for researchers and practitioners in AI and NLP by providing an accessible tool for generating high-quality knowledge graphs, though it is incremental as it builds on existing language model techniques.
The authors tackled the scarcity of high-quality knowledge graphs by developing KGGen, a tool that uses language models to extract knowledge graphs from plain text, achieving far superior performance compared to existing extractors as demonstrated on their new MINE benchmark.
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.