Review on the Construction and Application of Knowledge Graphs for Natural Disaster Emergency Response

In recent years, the frequent occurrence of extreme natural disasters globally has placed higher demands on the intelligence and efficiency of emergency management. As a structured semantic knowledge base, the knowledge graph provides a new technological pathway for natural disaster emergency response by virtue of its powerful capabilities in knowledge integration and reasoning. This paper systematically reviews the construction methods and application scenarios of knowledge graphs for natural disaster emergency response. Regarding construction, it focuses on key technologies such as multi-source heterogeneous knowledge acquisition, ontology modeling, knowledge extraction, and fusion, while also pointing out current challenges in domain knowledge injection, data scarcity, and dynamic updating. At the application level, it analyzes the practical value of knowledge graphs in core scenarios such as disaster assessment, emergency plan generation, and resource allocation coordination. Finally, this paper summarizes the shortcomings of existing research and outlines future development trends, including empowerment by large language models (LLMs), spatiotemporal reasoning, and human-machine collaborative decision-making, aiming to provide references for building more intelligent and robust natural disaster emergency response systems.