知识图-大型语言模型融合方法应急知识建议在天然气道
Na Xu1, Xi Chen2, Jinpei Luo3
1School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou, 221116, China.
Scientific reports
|March 1, 2026
概括
本研究介绍了气体道紧急情况的知识图 (KG) 和大语言模型 (LLM) 方法. 它通过提供准确的,特定于背景的信息来增强应急响应决策.
科学领域:
- 应急响应应对紧急情况的反应.
- 知识管理知识管理
- 人工智能的人工智能是人工智能.
背景情况:
- 燃气道紧急情况需要快速,准确的决策.
- 紧急情况的知识往往分散在非结构化数据源中.
- 现有的方法阻碍了对关键信息的快速获取.
研究的目的:
- 为天然气道紧急情况制定知识推方法.
- 协同结合知识图 (KG) 和大型语言模型 (LLM).
- 提高紧急决策的速度和质量.
主要方法:
- 从异质的紧急文件中构建一个域特定的KG,使用快速优化的LLMs.
- 通过基于语义相似性的向量搜索检索相关的子图.
- 动态增强LLM与检索KG知识来减少幻觉.
主要成果:
- 拟议的KG-LLM方法显著优于独立的LLM和其他架构.
- 在紧急知识的准确性,完整性和连贯性方面取得了明显的改进.
- 该方法为基于知识的决策支持提供了一个结构化和可解释的方法.
结论:
- 集成的KG-LLM方法增强了天然气道的应急响应.
- 这种方法为高风险场景中的知识可访问性提供了一个新的解决方案.
- 它在关键事件期间促进了更知情和及时的决策.
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