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Combining knowledge graphs and generative AI to explain the consequences of climatic events on the health of maasai herders
Acte de colloque

Combining knowledge graphs and generative AI to explain the consequences of climatic events on the health of maasai herders

Houdhem Assoudi, Vincent Armant, Amira Mouakher, Emmanuel Roux, Jean-Christophe Desconnets et Victor N. Mose
Actes des 23es Rencontres des Jeunes Chercheurs en Intelligence Artificielle
Rencontres des Jeunes Chercheurs en Intelligence Artificielle (Dijon, France, 30/06/2025–04/07/2025)
2025

Résumé

Maasai Community Health indicators Severe climate events Explainable AI Retrieval-augmented generation Knowledge graph Ontology
This PhD project, started in January 2025, aims to develop a knowledge-driven system to explain the impact of extreme climatic events and environmental changes on the health of Maasai communities in Kenya and Tanzania. It leverages knowledge graphs to structure and interconnect heterogeneous data sources (health, climate, local practices) and integrates a Retrieval-Augmented Generation (RAG) system. The knowledge graph serves as a structured repository, facilitating the extraction and organization of relevant information. The RAG system utilizes these structured insights to generate context-aware, traceable, and comprehensible explanations tailored to different stakeholders, including local communities, researchers, and policymakers. Conducted within the MOSAIC project, this research combines artificial intelligence, semantic web technologies, and environmental health studies to enhance scientific understanding and support informed decision-making.

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