Towards Enabling FAIR Dataspaces Using Large Language Models

TL;DR

This paper explores how Large Language Models can facilitate the adoption of FAIR dataspaces, addressing complexity challenges and proposing a research agenda for future exploration.

Contribution

It demonstrates the potential of LLMs in supporting FAIR dataspaces and outlines a research agenda for this emerging field.

Findings

LLMs can support FAIR dataspaces effectively

A concrete example illustrating LLM application in dataspaces

Proposes a research agenda for future work

Abstract

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.