Is dc:subject enough? A landscape on iconography and iconology statements of knowledge graphs in the semantic web
| DOI | https://doi.org/10.1108/JD-09-2022-0207 |
| Published date | 30 March 2023 |
| Date | 30 March 2023 |
| Pages | 115-136 |
| Subject Matter | Library & information science,Records management & preservation,Document management,Classification & cataloguing,Information behaviour & retrieval,Collection building & management,Scholarly communications/publishing,Information & knowledge management,Information management & governance,Information management,Information & communications technology,Internet |
| Author | Sofia Baroncini,Bruno Sartini,Marieke Van Erp,Francesca Tomasi,Aldo Gangemi |
Is dc:subject enough? A landscape
on iconography and iconology
statements of knowledge graphs
in the semantic web
Sofia Baroncini and Bruno Sartini
University of Bologna, Bologna, Italy
Marieke Van Erp
DHLab, KNAW, Amsterdam, The Netherlands, and
Francesca Tomasi and Aldo Gangemi
University of Bologna, Bologna, Italy
Abstract
Purpose –In the last few years, the size of Linked Open Data (LOD) describingartworks, in general or domain-
specific Knowledge Graphs (KGs), is gradually increasing. This provides (art-)historians and Cultural Heritage
professionals with a wealth of information to explore. Specifically, structured data about iconographical and
iconological (icon) aspects, i.e. information about the subjects, concepts and meanings of artworks, are
extremely valuable for the state-of-the-art of computational tools, e.g. content recognition through computer
vision. Nevertheless, a data quality evaluation for art domains, fundamental for data reuse, is still missing. The
purpose of this study is filling this gap with an overview of art-historical data quality in current KGs with a
focus on the icon aspects.
Design/methodology/approach –This study’s analyses are based on established KG evaluation
methodologies, adapted to the domain by addressing requirements from art historians’theories. The
authors first select several KGs according to Semantic Web principles. Then, the authors evaluate (1) their
structures’suitability to describe icon information through quantitative and qualitative assessment and (2)
their content, qualitatively assessed in terms of correctness and completeness.
Findings –This study’s results reveal several issues on the current expression of icon information in KGs. The
content evaluation shows that these domain-specific statements are generally correct but often not complete.
The incompleteness is confirmed by the structure evaluation, which highlights the unsuitability of the KG
schemas to describe icon information with the required granularity.
Originality/value –The main contribution of this work is an overview of the actual landscape of the icon
information expressed in LOD. Therefore, it is valuable to cultural institutions by providing them a first
domain-specificdata quality evaluation. Since this study’s results suggest that the selected domain information
is underrepresented in Semantic Web datasets, the authors highlight the need forthe creation and fostering of
such information to provide a more thorough art-historical dimension to LOD.
Keywords Knowledge graph evaluation, Iconology, Iconography, Visual works
Paper type Research paper
Iconography
and iconology
in LOD
115
© Sofia Baroncini, Bruno Sartini, Marieke Van Erp, Francesca Tomasi and Aldo Gangemi. Publis hed by
Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC
BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this
article (for both commercial and non-commercial purposes), subject to full attribution to the original
publication and authors. The full terms of this licence may be seen at http://creativecommons.org/
licences/by/4.0/legalcode
This work has been partially funded by the Emilia Romagna Region (grant agreement no. 462 25/
03/2019), the University of Bologna, and the SPICE EU H2020 Project 870811 within the program:
SOCIETAL CHALLENGES - Europe In A Changing World - Inclusive, Innovative And Reflective
Societies.
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/0022-0418.htm
Received 23 September 2022
Revised 5 February 2023
Accepted 16 February 2023
Journal of Documentation
Vol. 79 No. 7, 2023
pp. 115-136
Emerald Publishing Limited
0022-0418
DOI 10.1108/JD-09-2022-0207
1. Introduction
Recent years have witnessed a growing interest in linked open data describing Cultural
Heritage (Davis and Heravi, 2021). Despite many cultural institutions releasing their data
only in a simple tabular form, several knowledge graphs (KGs) are addressing the description
of artworks in a more structured, logical form [1]. Some of them, e.g. Wikidata (Vrande
ci
c and
Kr€
otzsch, 2014), have a general scope and are created in a collaborative way, while others
(e.g. ArCo (Carriero et al., 2019), Zeri and Lode (Daquino et al., 2017)), are generated by the
conversion of authoritative data from cultural institutions.
In this diversified setting, it is important to assess the coverage, accuracy and reliability of
the available data to allow their reuse for domain-specific purposes. While many studies
addressed the problem of KG evaluation methods, to the authors’knowledge, a survey on art
history information stored in KGs, comprehensive of an assessment of the data quality, is still
missing. Therefore, this work aims to evaluate the coverage of the content represented in
visual works over existing KGs, with a focus on iconographical and iconological aspects
(i.e. artistic subjects and their symbolic and cultural meanings). The phrase “iconographical
and iconological”will be referred to as icon from now on. We survey KG evaluation
methodologies and adapt some of their metrics to the considered domain of knowledge.
Furthermore, theories concerning the icon domain are reviewed to assess the extent to which
KGs cover information about visual items’subject and content description.
Semantic web technologies offer an opportunity to formally express semantically complex
information. For this reason, they are a suitable means to express fields of study as complex
as iconography and iconology at the required granularity.
Artwork contents should be analysed both isolated, i.e. by identifying relevant features
and associating them to features of other artworks (e.g. the study of patterns recurring in
different subjects (Wittkower, 1987;Warburg, 1999)). Therefore, the knowledge emerging
from an analytic approach is mostly missed when an artwork’s content is described just by a
general subject term.
The traditional sources of knowledge are natural language descriptions of artworks as
found in texts, but texts need knowledge extraction methods to enable further analysis and
interlinking, limiting the computational reuse of that knowledge (Sartini and Gangemi, 2021).
Another problem is the lack of advanced ontologies [2] that provide a detailed semantic
form to artwork description data. Only recently, a few ontologies have been designed to
express icon features (Carboni and de Luca, 2019) and cultural symbolism (Sartini et al., 2021),
opening the possibility to extract and represent KGs as required.
In addition, since iconographical–iconological analysis can potentially involve very
different types of cultural objects, often stored by different institutions, the major benefits of
storing information about this domain in KGs include at least:
(1) The opportunity to answer domain-specific questions through quantitative analysis
(e.g. which attributes and meanings were related to the mythological character of
Mercury across the centuries?);
(2) Accessing and querying interlinked information about worldwide objects that could
not otherwise be experienced together (e.g. all artworks with political implications
stored in different museums worldwide);
(3) Formallyexpressing the semantic complexity of the topic(e.g. the levelsof meanings of
an artwork and its relations to external resources, such as other artworks, texts, etc.).
By providing curated and reliable semantic data about this domain, we aim to help traditional
art historical research by offering new computational applications, pushing forward
quantitative studies already conducted on the art history field (e.g. Greenwald, 2021).
JD
79,7
116
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