Communication Dans Un Congrès Année : 2025

AI4DiTraRe: Towards LLM-Based Information Extraction for Standardising Climate Research Repositories

Résumé

In the petabyte-era of climate research, harmonising diverse environmental and geoscientific datasets is critical to improve data interoperability and support effectiveness of interdisciplinary studies. This paper presents an idea of designing an LLM-based tool to extract and standardize metadata from climate research repositories. The solution leverages the adaptability of LLMs that are able to understand contextual nuances. By addressing common inconsistencies such as varying parameters (observation types), units, and definitions, the proposed tool will significantly improve effective data integration. It will be the first step to facilitate the creation of a unified metadata schema adhering to the FAIR principles.

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hal-04957799 , version 1 (27-02-2025)

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Anna Jacyszyn, Shufan Jiang, Genet Asefa Gesese, Sven Hertling, Tobias Kerzenmacher, et al.. AI4DiTraRe: Towards LLM-Based Information Extraction for Standardising Climate Research Repositories. First AAAI Bridge on Artificial Intelligence for Scholarly Communication AI4SC, Feb 2025, Philadelphia, United States. ⟨10.5281/zenodo.14872358⟩. ⟨hal-04957799⟩
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