# Making Metadata More FAIR Using Large Language Models

arXiv: https://arxiv.org/abs/2307.13085 · HTML: https://mlsanity.com/paper/2307.13085/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 29 references that are in the map.
- First posted on arXiv 24 Jul 2023.

## Builds on (most cited first, 8 of 8)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models](https://mlsanity.com/paper/2303.10130/) (2023)
- [A Simple Standard for Sharing Ontological Mappings (SSSOM)](https://mlsanity.com/paper/2112.07051/) (2021)
- [Modeling community standards for metadata as templates makes data FAIR](https://mlsanity.com/paper/2208.02836/) (2022)
- [Aligning Biomedical Metadata with Ontologies Using Clustering and Embeddings](https://mlsanity.com/paper/1903.08206/) (2019)
- [The CEDAR Workbench: An Ontology-Assisted Environment for Authoring Metadata that Describe Scientific Experiments](https://mlsanity.com/paper/1905.06480/) (2019)
- [Using association rule mining and ontologies to generate metadata recommendations from multiple biomedical databases](https://mlsanity.com/paper/1903.09270/) (2019)

## Built on by (most cited first, 3 of 3)

- [A Systematic Framework for Enterprise Knowledge Retrieval: Leveraging LLM-Generated Metadata to Enhance RAG Systems](https://mlsanity.com/paper/2512.05411/) (2025)
- [Towards Enabling FAIR Dataspaces Using Large Language Models](https://mlsanity.com/paper/2403.15451/) (2024)
- [Flexible metadata harvesting for ecology using large language models](https://mlsanity.com/paper/2508.20115/) (2025)

## Abstract (arXiv)

With the global increase in experimental data artifacts, harnessing them in a unified fashion leads to a major stumbling block - bad metadata. To bridge this gap, this work presents a Natural Language Processing (NLP) informed application, called FAIRMetaText, that compares metadata. Specifically, FAIRMetaText analyzes the natural language descriptions of metadata and provides a mathematical similarity measure between two terms. This measure can then be utilized for analyzing varied metadata, by suggesting terms for compliance or grouping similar terms for identification of replaceable terms. The efficacy of the algorithm is presented qualitatively and quantitatively on publicly available research artifacts and demonstrates large gains across metadata related tasks through an in-depth study of a wide variety of Large Language Models (LLMs). This software can drastically reduce the human effort in sifting through various natural language metadata while employing several experimental datasets on the same topic.