A Semi-Automated Framework for Flood Ontology Construction with an Application in Risk Communication
Shenglin Li, Caleb Erickson, Michal Zajac, Xiaoming Guo, Qiuhua Duan, Jiaqi Gong · Water · 2025
Flash floods are increasingly frequent and severe, yet standard risk communication messages are often too generic and lack actionable guidance, causing them to be ignored. This research aims to enhance flood risk communication by first, developing a robust flood ontology using a novel semi-automated approach, and second, demonstrating its potential as a semantic foundation for translating complex data into clear, personalized public alerts. We introduce a semi-automated, human-in-the-loop ontology engineering strategy that integrates expert-defined schemas with Large Language Model (LLM)-driven expansion and refinement from authoritative sources. Evaluation results are twofold: (1) Technical metrics confirm our LLM-constructed ontology achieves superior relationship richness and expressiveness compared with existing disaster ontologies. (2) A proof-of-concept case study demonstrates the ontology’s potential by showing how its specific classes and relations (e.g., ‘neededForElderly’ relation linking the class ‘SpecialConsideration’ to ‘ElderlyCommunityMember’) can be used to generate targeted advice like “check on elderly neighbors”, transforming a generic alert into a clear and actionable message. Consequently, this research delivers two key contributions: a replicable and domain-adaptable methodology for semi-automated ontology construction and a practical demonstration of how such an ontology can bridge the critical gap between flood data and public understanding, empowering communities to respond more effectively.