Towards Enabling FAIR Dataspaces Using Large Language Models
Benedikt T. Arnold Johannes Theissen-Lipp Diego Collarana Christoph Lange Sandra Geisler Edward Curry Stefan Decker
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.