Emergency management urgently requires comprehensive knowledge while having a high possibility to go beyond individuals' cognitive scope. Therefore, artificial intelligence(AI) supported decision-making under that circumstance is of vital importance. Recent emerging large language models (LLM) provide a new direction for enhancing targeted machine intelligence. However, the utilization of LLM directly would inevitably introduce unreliable output for its inherent issue of hallucination and poor reasoning skills. In this work, we develop a system called Enhancing Emergency decision-making with Knowledge Graph and LLM (E-KELL), which provides evidence-based decision-making in various emergency stages. The study constructs a structured emergency knowledge graph and guides LLMs to reason over it via a prompt chain. In real-world evaluations, E-KELL receives scores of 9.06, 9.09, 9.03, and 9.09 in comprehensibility, accuracy, conciseness, and instructiveness from a group of emergency commanders and firefighters, demonstrating a significant improvement across various situations compared to baseline models. This work introduces a novel approach to providing reliable emergency decision support.

该研究论文介绍了一种名为E-KELL的系统，通过构建结构化的紧急情况知识图谱，并引导大型语言模型在此基础上进行推理，提供基于证据的紧急情况决策，取得了显著的改进和可靠的应急决策支持。

利用知识图谱和大型语言模型增强紧急决策