Exploring LLM Capabilities in Extracting DCAT-Compatible Metadata for Data Cataloging

TL;DR

This paper explores how large language models can automate the creation of high-quality, DCAT-compatible metadata for data catalogs, potentially reducing manual effort and improving data discovery processes.

Contribution

It demonstrates the effectiveness of LLMs in generating metadata and classifying data, highlighting the benefits of fine-tuning and prompting strategies for data cataloging.

Findings

LLMs can produce metadata comparable to human-created content.

Larger models outperform smaller ones in metadata generation.

Fine-tuning improves classification accuracy significantly.

Abstract

Efficient data exploration is crucial as data becomes increasingly important for accelerating processes, improving forecasts and developing new business models. Data consumers often spend 25-98 % of their time searching for suitable data due to the exponential growth, heterogeneity and distribution of data. Data catalogs can support and accelerate data exploration by using metadata to answer user queries. However, as metadata creation and maintenance is often a manual process, it is time-consuming and requires expertise. This study investigates whether LLMs can automate metadata maintenance of text-based data and generate high-quality DCAT-compatible metadata. We tested zero-shot and few-shot prompting strategies with LLMs from different vendors for generating metadata such as titles and keywords, along with a fine-tuned model for classification. Our results show that LLMs can generate metadata comparable to human-created content, particularly on tasks that require advanced semantic understanding. Larger models outperformed smaller ones, and fine-tuning significantly improves classification accuracy, while few-shot prompting yields better results in most cases. Although LLMs offer a faster and reliable way to create metadata, a successful application requires careful consideration of task-specific criteria and domain context.