# iText2KG: Incremental Knowledge Graphs Construction Using Large Language Models

arXiv: https://arxiv.org/abs/2409.03284 · HTML: https://mlsanity.com/paper/2409.03284/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 15 references that are in the map.
- First posted on arXiv 5 Sep 2024.

## Builds on (most cited first, 10 of 10)

- [Large Language Models on Graphs: A Comprehensive Survey](https://mlsanity.com/paper/2312.02783/) (2023)
- [LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities](https://mlsanity.com/paper/2305.13168/) (2023)
- [Text2KGBench: A Benchmark for Ontology-Driven Knowledge Graph Generation from Text](https://mlsanity.com/paper/2308.02357/) (2023)
- [From human experts to machines: An LLM supported approach to ontology and knowledge graph construction](https://mlsanity.com/paper/2403.08345/) (2024)
- [Zero-Shot Clinical Trial Patient Matching with LLMs](https://mlsanity.com/paper/2402.05125/) (2024)
- [Iterative Zero-Shot LLM Prompting for Knowledge Graph Construction](https://mlsanity.com/paper/2307.01128/) (2023)
- [Automated Construction of Theme-specific Knowledge Graphs](https://mlsanity.com/paper/2404.19146/) (2024)
- [Natural Language Processing for Information Extraction](https://mlsanity.com/paper/1807.02383/) (2018)
- [Knowledge Graph in Astronomical Research with Large Language Models: Quantifying Driving Forces in Interdisciplinary Scientific Discovery](https://mlsanity.com/paper/2406.01391/) (2024)
- [AttacKG+:Boosting Attack Knowledge Graph Construction with Large Language Models](https://mlsanity.com/paper/2405.04753/) (2024)

## Built on by (most cited first, 11 of 11)

- [AutoGraph-R1: End-to-End Reinforcement Learning for Knowledge Graph Construction](https://mlsanity.com/paper/2510.15339/) (2025)
- [MedKGent: A Large Language Model Agent Framework for Constructing Temporally Evolving Medical Knowledge Graph](https://mlsanity.com/paper/2508.12393/) (2025)
- [KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration](https://mlsanity.com/paper/2602.20135/) (2026)
- [DIAL-KG: Schema-Free Incremental Knowledge Graph Construction via Dynamic Schema Induction and Evolution-Intent Assessment](https://mlsanity.com/paper/2603.20059/) (2026)
- [OpenGloss: A Synthetic Encyclopedic Dictionary and Semantic Knowledge Graph](https://mlsanity.com/paper/2511.18622/) (2025)
- [InvertiTune: High-Quality Data Synthesis for Cost-Effective Single-Shot Text-to-Knowledge Graph Generation](https://mlsanity.com/paper/2512.03197/) (2025)
- [GenAI-Driven Approach to RISC-V Supply Chain Exploration](https://mlsanity.com/paper/2605.15223/) (2026)
- [RAGA: Reading-And-Graph-building-Agent for Autonomous Knowledge Graph Construction and Retrieval-Augmented Generation](https://mlsanity.com/paper/2605.17072/) (2026)
- [FAIR GraphRAG: A Retrieval-Augmented Generation Approach for Semantic Data Analysis](https://mlsanity.com/paper/2607.11464/) (2026)
- [C-Unseen: Weak Signal Detection in Dynamic Temporal Knowledge Graphs via LLM Reasoning](https://mlsanity.com/paper/2608.26870/) (2026)
- [Extracting ontology-compliant knowledge from scientific text describing irradiated materials using large language models](https://mlsanity.com/paper/2609.17291/) (2026)

## Abstract (arXiv)

Most available data is unstructured, making it challenging to access valuable information. Automatically building Knowledge Graphs (KGs) is crucial for structuring data and making it accessible, allowing users to search for information effectively. KGs also facilitate insights, inference, and reasoning. Traditional NLP methods, such as named entity recognition and relation extraction, are key in information retrieval but face limitations, including the use of predefined entity types and the need for supervised learning. Current research leverages large language models' capabilities, such as zero- or few-shot learning. However, unresolved and semantically duplicated entities and relations still pose challenges, leading to inconsistent graphs and requiring extensive post-processing. Additionally, most approaches are topic-dependent. In this paper, we propose iText2KG, a method for incremental, topic-independent KG construction without post-processing. This plug-and-play, zero-shot method is applicable across a wide range of KG construction scenarios and comprises four modules: Document Distiller, Incremental Entity Extractor, Incremental Relation Extractor, and Graph Integrator and Visualization. Our method demonstrates superior performance compared to baseline methods across three scenarios: converting scientific papers to graphs, websites to graphs, and CVs to graphs.