Towards Enabling FAIR Dataspaces Using Large Language Models
This work addresses the problem of making dataspaces more accessible and cost-effective for sectors like culture, but it is incremental as it primarily explores potential rather than delivering new methods or results.
The paper tackles the challenge of adopting FAIR dataspaces due to the complexity of Semantic Web technologies, and demonstrates the potential of Large Language Models to support this adoption with a concrete example, while proposing a research agenda for the field.
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.