Can LLMs be Good Graph Judge for Knowledge Graph Construction?

• URL: http://arxiv.org/abs/2411.17388v3
• Date: Tue, 20 May 2025 16:24:22 GMT
• Title: Can LLMs be Good Graph Judge for Knowledge Graph Construction?
• Authors: Haoyu Huang, Chong Chen, Zeang Sheng, Yang Li, Wentao Zhang,
• Abstract summary: We propose textbfGraphJudge, a KG construction framework to address the aforementioned challenges.<n>In this framework, we designed an entity-centric strategy to eliminate the noise information in the documents.<n>And we fine-tuned a LLM as a graph judge to finally enhance the quality of generated KGs.
• Score: 24.752904398871127
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In real-world scenarios, most of the data obtained from the information retrieval (IR) system is unstructured. Converting natural language sentences into structured Knowledge Graphs (KGs) remains a critical challenge. We identified three limitations with respect to existing KG construction methods: (1) There could be a large amount of noise in real-world documents, which could result in extracting messy information. (2) Naive LLMs usually extract inaccurate knowledge from some domain-specific documents. (3) Hallucination phenomenon cannot be overlooked when directly using LLMs to construct KGs. In this paper, we propose \textbf{GraphJudge}, a KG construction framework to address the aforementioned challenges. In this framework, we designed an entity-centric strategy to eliminate the noise information in the documents. And we fine-tuned a LLM as a graph judge to finally enhance the quality of generated KGs. Experiments conducted on two general and one domain-specific text-graph pair datasets demonstrate state-of-the-art performance against various baseline methods with strong generalization abilities. Our code is available at \href{https://github.com/hhy-huang/GraphJudge}{https://github.com/hhy-huang/GraphJudge}.

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