Domain question-driven Linked Data modeling

The case study of iconological studies

Authors

DOI:

https://doi.org/10.6092/issn.2532-8816/21213

Keywords:

AIUCD2023, iconological studies, domain knowledge representation, question-driven approach, Linked Open Data

Abstract

Currently, there is an increasing interest in experimenting with applications of computer science to humanistic disciplines. Although some domains successfully integrated some digital tools and techniques in their methods, some other domains had a slower, narrow integration. This paper addresses the challenge of experimenting with the translation of qualitative research into a quantitative one, by presenting the experience of the creation of a domain-specific Linked Open Data (LOD) dataset of iconographic and iconological art studies, namely, the Iconology Dataset. The peculiarity of the process adopted lies in its strong grounding in the theoretical framework of the domain, as it followed an ontological modeling according to the key theories proposed and a modeling and analysis through the scholars’ key research questions. For the sake of enhancing the transfer of the approach to other studies, we refined it in 5 phases and presented a general description of them. For its characteristics of lack of formalization and interdisciplinary nature, we argue that the approach developed for the iconographical-iconological research field can be relevant for the methodological transfer to other domains.

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Published

2025-07-14

How to Cite

Baroncini, S., Daquino, M., & Tomasi, F. (2025). Domain question-driven Linked Data modeling: The case study of iconological studies. Umanistica Digitale, 9(20), 459–492. https://doi.org/10.6092/issn.2532-8816/21213