Text classification of column headers with a controlled vocabulary: leveraging LLMs for metadata enrichment
Margherita Martorana, Tobias Kuhn, Lise Stork, Jacco van Ossenbruggen
Traditional dataset retrieval systems index on metadata information rather than on the data values. Thus relying primarily on manual annotations and high-quality metadata, processes known to be labour-intensive and challenging to automate. We propose a method to support metadata enrichment with topic annotations of column headers using three Large Language Models (LLMs): ChatGPT-3.5, GoogleBard and GoogleGemini. We investigate the LLMs ability to classify column headers based on domain-specific topics from a controlled vocabulary. We evaluate our approach by assessing the internal consistency of the LLMs, the inter-machine alignment, and the human-machine agreement for the topic classification task. Additionally, we investigate the impact of contextual information (i.e. dataset description) on the classification outcomes. Our results suggest that ChatGPT and GoogleGemini outperform GoogleBard for internal consistency as well as LLM-human-alignment. Interestingly, we found that context had no impact on the LLMs performances. This work proposes a novel approach that leverages LLMs for text classification using a controlled topic vocabulary, which has the potential to facilitate automated metadata enrichment, thereby enhancing dataset retrieval and the Findability, Accessibility, Interoperability and Reusability (FAIR) of research data on the Web.