# KGGen: Extracting Knowledge Graphs from Plain Text with Language Models

arXiv: https://arxiv.org/abs/2502.09956 · HTML: https://mlsanity.com/paper/2502.09956/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 32 references that are in the map.
- First posted on arXiv 14 Feb 2025.

## Builds on (most cited first, 9 of 9)

- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Extract, Define, Canonicalize: An LLM-based Framework for Knowledge Graph Construction](https://mlsanity.com/paper/2404.03868/) (2024)
- [A Decade of Knowledge Graphs in Natural Language Processing: A Survey](https://mlsanity.com/paper/2210.00105/) (2022)
- [From human experts to machines: An LLM supported approach to ontology and knowledge graph construction](https://mlsanity.com/paper/2403.08345/) (2024)
- [A Study of the Quality of Wikidata](https://mlsanity.com/paper/2107.00156/) (2021)
- [Large Language Models for Biomedical Knowledge Graph Construction: Information extraction from EMR notes](https://mlsanity.com/paper/2301.12473/) (2023)
- [Triple Trustworthiness Measurement for Knowledge Graph](https://mlsanity.com/paper/1809.09414/) (2018)
- [Efficient Knowledge Graph Accuracy Evaluation](https://mlsanity.com/paper/1907.09657/) (2019)
- [KGrEaT: A Framework to Evaluate Knowledge Graphs via Downstream Tasks](https://mlsanity.com/paper/2308.10537/) (2023)

## Built on by (most cited first, 30 of 49)

- [LLM-empowered knowledge graph construction: A survey](https://mlsanity.com/paper/2510.20345/) (2025)
- [Automated Knowledge Graph Construction using Large Language Models and Sentence Complexity Modelling](https://mlsanity.com/paper/2509.17289/) (2025)
- [AutoGraph-R1: End-to-End Reinforcement Learning for Knowledge Graph Construction](https://mlsanity.com/paper/2510.15339/) (2025)
- [Wikontic: Constructing Wikidata-Aligned, Ontology-Aware Knowledge Graphs with Large Language Models](https://mlsanity.com/paper/2512.00590/) (2025)
- [Relink: Constructing Query-Driven Evidence Graph On-the-Fly for GraphRAG](https://mlsanity.com/paper/2601.07192/) (2026)
- [RAKG:Document-level Retrieval Augmented Knowledge Graph Construction](https://mlsanity.com/paper/2504.09823/) (2025)
- [ODKE+: Ontology-Guided Open-Domain Knowledge Extraction with LLMs](https://mlsanity.com/paper/2509.04696/) (2025)
- [LightKGG: Simple and Efficient Knowledge Graph Generation from Textual Data](https://mlsanity.com/paper/2510.23341/) (2025)
- [SAM Guided Semantic and Motion Changed Region Mining for Remote Sensing Change Captioning](https://mlsanity.com/paper/2511.21420/) (2025)
- [Incentivizing In-depth Reasoning over Long Contexts with Process Advantage Shaping](https://mlsanity.com/paper/2601.12465/) (2026)
- [Task-Awareness Improves LLM Generations and Uncertainty](https://mlsanity.com/paper/2601.21500/) (2026)
- [Ontology-to-tools compilation for executable semantic constraint enforcement in LLM agents](https://mlsanity.com/paper/2602.03439/) (2026)
- [Structuring the Unstructured: A Systematic Review of Text-to-Structure Generation for Agentic AI with a Universal Evaluation Framework](https://mlsanity.com/paper/2508.12257/) (2025)
- [Knowledge Graph Representations for LLM-Based Policy Compliance Reasoning](https://mlsanity.com/paper/2604.27713/) (2026)
- [Reviewing Clinical Knowledge in Medical Large Language Models: Training and Beyond](https://mlsanity.com/paper/2502.20988/) (2025)
- [CLARIFY: A Specialist-Generalist Framework for Accurate and Lightweight Dermatological Visual Question Answering](https://mlsanity.com/paper/2508.18430/) (2025)
- [Are Large Language Models Effective Knowledge Graph Constructors?](https://mlsanity.com/paper/2510.11297/) (2025)
- [AtlasKV: Augmenting LLMs with Billion-Scale Knowledge Graphs in 20GB VRAM](https://mlsanity.com/paper/2510.17934/) (2025)
- [HalluGraph: Auditable Hallucination Detection for Legal RAG Systems via Knowledge Graph Alignment](https://mlsanity.com/paper/2512.01659/) (2025)
- [Ontology-Based Knowledge Graph Framework for Industrial Standard Documents via Hierarchical and Propositional Structuring](https://mlsanity.com/paper/2512.08398/) (2025)
- [Instructor-Aligned Knowledge Graphs for Personalized Learning](https://mlsanity.com/paper/2602.17111/) (2026)
- [AgentCAT: An LLM Agent for Extracting and Analyzing Catalytic Reaction Data from Chemical Engineering Literature](https://mlsanity.com/paper/2602.18479/) (2026)
- [Towards Automated Ontology Generation from Unstructured Text: A Multi-Agent LLM Approach](https://mlsanity.com/paper/2604.23090/) (2026)
- [Query Symbolically or Retrieve Semantically? A Dataset and Method for Semi-Structured Question Answering](https://mlsanity.com/paper/2605.27164/) (2026)
- [Better Later Than Sooner: Neuro-Symbolic Knowledge Graph Construction via Ontology-grounded Post-extraction Correction](https://mlsanity.com/paper/2605.29168/) (2026)
- [EMERGE: A Benchmark for Updating Knowledge Graphs with Emerging Textual Knowledge](https://mlsanity.com/paper/2507.03617/) (2025)
- [BambooKG: A Neurobiologically-inspired Frequency-Weight Knowledge Graph](https://mlsanity.com/paper/2510.25724/) (2025)
- [AI Consciousness and Existential Risk](https://mlsanity.com/paper/2511.19115/) (2025)
- [InvertiTune: High-Quality Data Synthesis for Cost-Effective Single-Shot Text-to-Knowledge Graph Generation](https://mlsanity.com/paper/2512.03197/) (2025)
- [gHAWK: Local and Global Structure Encoding for Scalable Training of Graph Neural Networks on Knowledge Graphs](https://mlsanity.com/paper/2512.08274/) (2025)

## Abstract (arXiv)

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 (textttpip 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.