Leveraging Content-based Metadata in Catalogs to Improve Discoverability in Data Spaces
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Department of Software and Computing Systems, University of Alicante, Carretera San Vicente del Raspeig s/n, 03690 San Vicente del Raspeig, Spain
adriana.morejon@ua.es (corresponding author), alberto.berenguer@ua.es, lucia.espona@ua.es,
dtd@ua.es, jnmazon@ua.es
Abstract
Data spaces facilitate sovereignty-preserving data sharing within federated ecosystems; however, dataset discovery is often constrained by catalogs that rely primarily on high-level metadata standards such as DCAT-AP. These models typically lack structural and content-level details, which impedes fine-grained relevance assessment and complicates both internal dataset discovery and the federation of external open data into data spaces. This paper introduces cbmDCAT-AP, a backward-compatible extension of DCAT-AP that integrates content-based metadata as primary catalog elements. The proposed extension augments dataset descriptions with field-level semantics, and representative sample values, thereby enabling content-informed discovery without disclosing entire datasets. Furthermore, a clustering-based reduction technique is proposed to generate representative samples values through the use of word embeddings and K-means clustering. Finally, an end-to-end evaluation across two scenarios (internal dataset discovery and external open data selection) has been conducted to evaluate and validate our proposed extension of DCAT-AP.
Key words
Data Space, Metadata, Catalog, DCAT-AP, Discoverability, Open Data
Digital Object Identifier (DOI)
https://doi.org/10.2298/CSIS260301046M
Publication information
Volume 23, Issue 4 (September 2026)
Year of Publication: 2026
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium
Full text
Available in PDF
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How to cite
Morejón, A., Berenguer, A., de Espona, L., Tomás, D., Mazón, J.N.: Leveraging Content-based Metadata in Catalogs to Improve Discoverability in Data Spaces. Computer Science and Information Systems, 23(4), 1395–1430 (2026). https://doi.org/10.2298/CSIS260301046M
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