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Disaster Storylines and Knowledge Graphs from Global News with Large Language Models and Retrieval-Augmented

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Summary
This summary is machine-generated.

A new dataset details over 3,000 global disaster events (2014-2024) using AI. This resource enhances disaster risk management and multi-hazard analysis with openly available data and knowledge graphs.

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Area of Science:

  • Disaster Science
  • Data Science
  • Artificial Intelligence

Background:

  • Traditional disaster records often miss complex relationships and dynamics.
  • A comprehensive, structured dataset is needed for advanced risk assessment.
  • Existing resources like Hazard Information Profiles (HIPs) can be complemented.

Purpose of the Study:

  • To create a large-scale, structured dataset of global disaster events.
  • To enable deeper analysis of disaster characteristics, drivers, impacts, and responses.
  • To advance data-driven disaster scenario modeling and risk management.

Main Methods:

  • Utilized the Emergency Events Database (EM-DAT) for event data (2014-2024).
  • Employed a pipeline combining Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) for semantic news extraction from the Europe Media Monitor (EMM).
  • Generated structured storylines and knowledge graphs for each event, with expert and independent validation of extracted data.

Main Results:

  • Compiled a dataset of over 3,000 global disaster events with detailed, structured information.
  • Developed automatically generated knowledge graphs capturing hazard dynamics and human-environment interactions.
  • Quantified precision and inter-annotator agreement for extracted data, ensuring reliability.

Conclusions:

  • The dataset provides a novel resource for retrospective analysis and multi-hazard risk assessment.
  • Openly available data, code, and an interactive dashboard facilitate exploration and application.
  • This work supports improved disaster scenario modeling and decision-making in disaster risk management.