TY - JOUR
T1 - Towards Enabling FAIR Dataspaces Using Large Language Models
AU - Arnold, Benedikt T.
AU - Theissen-Lipp, Johannes
AU - Collarana, Diego
AU - Lange, Christoph
AU - Geisler, Sandra
AU - Curry, Edward
AU - Decker, Stefan
N1 - Publisher Copyright:
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Dataspaces
KW - FAIR Data Principles
KW - Large Language Models
UR - https://www.scopus.com/pages/publications/85196296490
M3 - Conference article
AN - SCOPUS:85196296490
SN - 1613-0073
VL - 3705
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
T2 - 2nd International Workshop on Semantics in Dataspaces, SDS 2024
Y2 - 26 May 2024
ER -