About This Document
- sl:arxiv_author :
- sl:arxiv_firstAuthor : Belinda Mo
- sl:arxiv_num : 2502.09956
- sl:arxiv_published : 2025-02-14T07:28:08Z
- sl:arxiv_summary : 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.@en
- sl:arxiv_title : KGGen: Extracting Knowledge Graphs from Plain Text with Language Models@en
- sl:arxiv_updated : 2025-02-14T07:28:08Z
- sl:bookmarkOf : https://arxiv.org/abs/2502.09956
- sl:creationDate : 2025-02-18
- sl:creationTime : 2025-02-18T15:07:20Z
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