Paper

KGGen: An Open-Source Framework for Extracting High-Quality Knowledge Graphs (KGs) from Text

2026.01.17

·Web·by 네루

#Knowledge Graph#NLP#Language Models#Open Source

Key Points

1The emerging focus on foundation models for Knowledge Graphs (KGs) faces a significant hurdle due to the fundamental scarcity of high-quality KG data.
2Currently, the most prominent KGs are derived primarily through labor-intensive human labeling, rule-based pattern matching, or earlier Natural Language Processing methods.
3This reliance on traditional extraction techniques contributes to the limited availability of knowledge-graph data.

The paper, titled "KGGen: Extracting Knowledge Graphs from Plain Text with Language Models," addresses the fundamental challenge of scarcity in knowledge-graph (KG) data. It highlights that widely recognized knowledge graphs have predominantly been constructed through labor-intensive human labeling, reliance on pattern-matching rules, or utilization of earlier Natural Language Processing (NLP) techniques. KGGen aims to mitigate this data scarcity by leveraging Language Models (LMs) to extract knowledge graphs directly from plain text sources.

Paper

2026.01.17

·Web·by 네루

#Knowledge Graph#NLP#Language Models#Open Source

1The emerging focus on foundation models for Knowledge Graphs (KGs) faces a significant hurdle due to the fundamental scarcity of high-quality KG data.
2Currently, the most prominent KGs are derived primarily through labor-intensive human labeling, rule-based pattern matching, or earlier Natural Language Processing methods.
3This reliance on traditional extraction techniques contributes to the limited availability of knowledge-graph data.

The paper, titled "KGGen: Extracting Knowledge Graphs from Plain Text with Language Models," addresses the fundamental challenge of scarcity in knowledge-graph (KG) data. It highlights that widely recognized knowledge graphs have predominantly been constructed through labor-intensive human labeling, reliance on pattern-matching rules, or utilization of earlier Natural Language Processing (NLP) techniques. KGGen aims to mitigate this data scarcity by leveraging Language Models (LMs) to extract knowledge graphs directly from plain text sources.