Can LLMs be Good Graph Judge for Knowledge Graph Construction?
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866912606704369664 |
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| author | Huang, Haoyu Chen, Chong Sheng, Zeang Li, Yang Zhang, Wentao |
| author_facet | Huang, Haoyu Chen, Chong Sheng, Zeang Li, Yang Zhang, Wentao |
| contents | 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}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_17388 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Can LLMs be Good Graph Judge for Knowledge Graph Construction? Huang, Haoyu Chen, Chong Sheng, Zeang Li, Yang Zhang, Wentao Computation and Language Artificial Intelligence 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}. |
| title | Can LLMs be Good Graph Judge for Knowledge Graph Construction? |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2411.17388 |