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arXiv · 2311.08732

Enhancing Emergency Decision-making with Knowledge Graphs and Large Language Models

Minze Chen·Zhenxiang Tao·Weitong Tang·Tingxin Qin·Rui Yang·Chunli Zhu
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Abstract

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.

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Minze Chen, Zhenxiang Tao, Weitong Tang, Tingxin Qin, Rui Yang, Chunli Zhu. 2023-11-15. Enhancing Emergency Decision-making with Knowledge Graphs and Large Language Models. https://arxiv.org/abs/2311.08732

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