Automatic Metadata Extraction Leveraging Large Language Models in Digital Humanities

DCAT-based data ecosystems, such as open data portals and data spaces, have shown their potential to foster data economy by supporting the FAIR (Findability, Accessibility, Interoperability, Reusability) principles. Nevertheless, there are domains where metadata are tailored to specific semantics of the domain, resulting in the absence of DCAT-based catalogs that adhere to FAIR principles. A particularly relevant case is that of the digital humanities, where texts encoded in TEI (Text Encoding Initiative) constitute a consolidated standard in the field of literature. However, TEI metadata are not always well aligned with the FAIR principles, nor easily integrated into interoperable catalogs that enable seamless combination with external datasets. To address this gap, our approach aims to (i) generate DCAT catalogs derived from TEI by identifying which metadata can be mapped and how, and (ii) explore the use of Large Language Models (LLMs) to assist in the generation and enrichment of metadata when transforming TEI to DCAT. Our approach contributes to catalog-level harmonization, enabling domain-specific standards such as TEI to be aligned with cross-domain standards like DCAT, thus facilitating adherence to FAIR principles.