用大模型从文本生成高质量知识图谱，解决数据稀缺问题
KGGen: Extracting Knowledge Graphs from Plain Text with Language Models
- 用语言模型生成知识图谱，通过实体聚类减少稀疏性
- 在新基准MINE上性能显著优于现有工具
- 开源为Python库，适合研究者和开发者快速构建图谱
近年来，构建知识图谱基础模型引发了对知识图谱数据稀缺性的关注。当前主流知识图谱多依赖人工标注、模式匹配或早期NLP技术提取，人工标注资源有限，自动提取结果质量参差。我们提出KGGen——一种文本到知识图谱的生成工具，利用语言模型从纯文本中生成高质量图谱。不同于传统提取器，KGGen通过聚类相关实体来降低图谱稀疏性。该工具已作为Python库发布（pip install kg-gen），便于广泛使用。同时，我们构建首个评估基准MINE（Measure of Information in Nodes and Edges），用于测试提取器从文本生成有用图谱的能力。在该基准上，KGGen表现远超现有方法。
原文摘要 · Abstract (English)
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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