In this work, we introduce ICON, an ontology that models artistic interpretations of artworks’ subject matter (i.e. iconographies) and meanings (i.e. symbols, iconological aspects). Developed by conceptualizing authoritative knowledge and notions taken from Panofsky’s levels of interpretation theory, ICON ontology focuses on the granularity of interpretations. It can be used to describe an interpretation of an artwork from the Pre-iconographical, Icongraphical, and Iconological levels. Its main classes have been aligned to ontologies that come from the domains of cultural descriptions (ArCo, CIDOC-CRM, VIR), semiotics (DOLCE), bibliometrics (CITO), and symbolism (Simulation Ontology), to grant a robust schema that can be extendable using additional classes and properties coming from these ontologies. The ontology was evaluated through competency questions that range from simple recognition on a specific level of interpretation to complex scenarios. Data written using this model was compared to state-of-the-art ontologies and schemas to both highlight the current lack of a domain-specific ontology on art interpretation and show how our work fills some of the current gaps. The ontology is openly available and compliant with FAIR principles. With our ontology, we hope to encourage digital art historians working for cultural institutions in making more detailed linked open data about the content of their artefacts, to exploit the full potential of Semantic Web in linking artworks through not only subjects and common metadata, but also specific symbolic interpretations, intrinsic meanings, and the motifs through which their subjects are represented. Additionally, by basing our work on theories made by different art history scholars in the last century, we make sure that their knowledge and studies will not be lost in the transition to the digital, linked open data era.

Sartini, B., Baroncini, S., van Erp, M., Tomasi, F., Gangemi, A. (2023). ICON: an Ontology for Comprehensive Artistic Interpretations. ACM JOURNAL ON COMPUTING AND CULTURAL HERITAGE, 16(3), 1-38 [10.1145/3594724].

ICON: an Ontology for Comprehensive Artistic Interpretations

Sartini, Bruno
Co-primo
;
Baroncini, Sofia
Co-primo
;
Tomasi, Francesca;Gangemi, Aldo
2023

Abstract

In this work, we introduce ICON, an ontology that models artistic interpretations of artworks’ subject matter (i.e. iconographies) and meanings (i.e. symbols, iconological aspects). Developed by conceptualizing authoritative knowledge and notions taken from Panofsky’s levels of interpretation theory, ICON ontology focuses on the granularity of interpretations. It can be used to describe an interpretation of an artwork from the Pre-iconographical, Icongraphical, and Iconological levels. Its main classes have been aligned to ontologies that come from the domains of cultural descriptions (ArCo, CIDOC-CRM, VIR), semiotics (DOLCE), bibliometrics (CITO), and symbolism (Simulation Ontology), to grant a robust schema that can be extendable using additional classes and properties coming from these ontologies. The ontology was evaluated through competency questions that range from simple recognition on a specific level of interpretation to complex scenarios. Data written using this model was compared to state-of-the-art ontologies and schemas to both highlight the current lack of a domain-specific ontology on art interpretation and show how our work fills some of the current gaps. The ontology is openly available and compliant with FAIR principles. With our ontology, we hope to encourage digital art historians working for cultural institutions in making more detailed linked open data about the content of their artefacts, to exploit the full potential of Semantic Web in linking artworks through not only subjects and common metadata, but also specific symbolic interpretations, intrinsic meanings, and the motifs through which their subjects are represented. Additionally, by basing our work on theories made by different art history scholars in the last century, we make sure that their knowledge and studies will not be lost in the transition to the digital, linked open data era.
2023
Sartini, B., Baroncini, S., van Erp, M., Tomasi, F., Gangemi, A. (2023). ICON: an Ontology for Comprehensive Artistic Interpretations. ACM JOURNAL ON COMPUTING AND CULTURAL HERITAGE, 16(3), 1-38 [10.1145/3594724].
Sartini, Bruno; Baroncini, Sofia; van Erp, Marieke; Tomasi, Francesca; Gangemi, Aldo
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/925537
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