ICON: an Ontology for Comprehensive Artistic Interpretations

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

Analysing the Evolution of Community-Driven (Sub-)Schemas within Wikidata

Overview of the approach presented in our position paper "Analysing the Evolution of Community-Driven (Sub-)Schemas within Wikidata", accepted at the Wikidata Workshop 2022

Analysing the Evolution of Community-Driven (Sub-)Schemas within Wikidata

Wikidata is a collaborative knowledge graph not structured according to predefined ontologies. Its schema evolves in a bottom-up approach defined by its users. In this paper, we propose a methodology to investigate how semantics develop in sub-schemas used by particular, domain-specific communities within the Wikidata knowledge graph: (i) we provide an approach to identify the domain sub-schema from a set of given classes and its related community, considered domain-specific; (ii) we propose an approach for analysing the such identified sub-schemas and communities, including their evolution over time. Finally, we suggest further possible analyses that would give better insights in (i) the communities themselves, (ii) the KG vocabulary accuracy, quality and its evolution over time according to domain areas, raising the potential of Wikidata improvement and its re-use by domain experts