Enhancing Emergency Decision-making with Knowledge Graphs and Large
  Language Models
Minze Chen Zhenxiang Tao Weitong Tang Tingxin Qin Rui Yang Chunli Zhu
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.