工矿自动化2025，Vol.51Issue(2)：76-83,105,9.DOI:10.13272/j.issn.1671-251x.2024080031
基于大语言模型的矿山事故知识图谱构建
Construction of a mine accident knowledge graph based on Large Language Models
摘要
Abstract
Current methods for constructing knowledge graphs in the field of mining require a large amount of manually labeled high-quality supervised data during the pre-training stage,resulting in high labor costs and low efficiency.Large Language Models(LLMs)can significantly improve the quality and efficiency of information extraction with only a small amount of manually labeled high-quality data.However,the prompt-based approach in LLMs suffers from catastrophic forgetting.To address this issue,graph-structured information was embedded into the prompt template and a Graph-Structured Prompt was proposed.By integrating this prompt into the LLM,high-quality construction of a mine accident knowledge graph based on the LLM was achieved.First,publicly available mine accident reports were collected from the Coal Mine Safety Production Network and preprocessed through formatting corrections and redundant information removal.Next,the LLM was utilized to extract knowledge embedded in the accident reports and K-means clustering was used to classify entities and relationships,thereby completing the construction of the mine accident ontology.Then,a small amount of data were labeled based on the ontology,which was used for LLM training and fine-tuning.Finally,the LLM embedded with the Graph-Structured Prompt was employed for information extraction,instantiating entity-relation triples to construct the mine accident knowledge graph.Experimental results showed that LLMs outperformed the Universal Information Extraction(UIE)model in entity and relationship extraction tasks.Moreover,the LLM embedded with the Graph-Structured Prompt achieved higher precision,recall,and F1 scores compared to those without it.关键词
矿山事故/知识图谱/大语言模型/图结构Prompt/本体构建/信息抽取Key words
mine accident/knowledge graph/Large Language Model/Graph-Structured Prompt/ontology construction/information extraction分类
矿山工程引用本文复制引用
张朋杨,生龙,王巍,魏忠诚,赵继军..基于大语言模型的矿山事故知识图谱构建[J].工矿自动化,2025,51(2):76-83,105,9.基金项目
国家自然科学基金资助项目(61802107) （61802107）
河北省高等学校科学技术研究项目(ZD2020171) （ZD2020171）
河北省省级科技计划资助项目(22567624H). （22567624H）
