Dataspaces have recently gained adoption across various sectors, including traditionally less digitized domains such as culture. Leveraging Semantic Web technologies helps to make dataspaces FAIR, but their complexity poses a significant challenge to the adoption of dataspaces and increases their cost. The advent of Large Language Models (LLMs) raises the question of how these models can support the adoption of FAIR dataspaces. In this work, we demonstrate the potential of LLMs in dataspaces with a concrete example. We also derive a research agenda for exploring this emerging field.
@article{arxiv.2403.15451,
title = {Towards Enabling FAIR Dataspaces Using Large Language Models},
author = {Benedikt T. Arnold and Johannes Theissen-Lipp and Diego Collarana and Christoph Lange and Sandra Geisler and Edward Curry and Stefan Decker},
journal= {arXiv preprint arXiv:2403.15451},
year = {2024}
}