# Towards Enabling FAIR Dataspaces Using Large Language Models

arXiv: https://arxiv.org/abs/2403.15451 · HTML: https://mlsanity.com/paper/2403.15451/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 15 references that are in the map.
- First posted on arXiv 18 Mar 2024.

## Builds on (most cited first, 6 of 6)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://mlsanity.com/paper/2306.08302/) (2023)
- [Grammar-Constrained Decoding for Structured NLP Tasks without Finetuning](https://mlsanity.com/paper/2305.13971/) (2023)
- [Efficient LLM Inference on CPUs](https://mlsanity.com/paper/2311.00502/) (2023)
- [Making Metadata More FAIR Using Large Language Models](https://mlsanity.com/paper/2307.13085/) (2023)

## Built on by (most cited first, 2 of 2)

- [Exploring LLM Capabilities in Extracting DCAT-Compatible Metadata for Data Cataloging](https://mlsanity.com/paper/2507.05282/) (2025)
- [FAIR GraphRAG: A Retrieval-Augmented Generation Approach for Semantic Data Analysis](https://mlsanity.com/paper/2607.11464/) (2026)

## Abstract (arXiv)

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.