Making Metadata More FAIR Using Large Language Models
Sowmya S. Sundaram Mark A. Musen
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.