Abstract:
The culture domain faces challenges in data interoperability due to heterogeneous data models, lack of standardization, and fragmented datasets.
Particularly in the performing arts sector, the provisioning of theater showtimes remains a heterogeneous, labor-intensive, and time-consuming process, which limits the FAIRness (Findability, Accessibility, Interoperability, Reusability) of play schedules (showtimes).
This limits the full exploitation of showtimes data, reducing its potential to drive innovative solutions.
Consequently, this fragmented and ad-hoc approach negatively impacts occupancy rates, disappoints audiences, and prevents the seamless operation of the sector.
Moreover, there is currently no standardized mechanism or infrastructure to protect the rights and sovereignty of cultural institutions and artists.
To address these issues, we introduce the Culture Information Model (Culture IM), an extensible, ontology-based framework designed to enable structured data representation and interoperability across performing arts theaters and beyond.
Culture IM follows the Semantic Web and Linked Data principles, integrating standards such as Schema.org and DCAT, while also supporting scenario-specific adaptations through application profiles (APs).
Its application through the sovereignty-preserving data management infrastructure of dataspaces ensures data interoperability, controlled data access, and compliance with FAIR principles.
This paper presents the Culture IM, comprising reusable semantic artifacts such as ontologies, controlled vocabularies, and APs; SIM, a novel semantic information modeling framework for domain-specific dataspaces; and AP-first, a novel iterative, user-friendly, and application-focused methodology that places APs at the center of the information modeling process and was employed for the development of Culture IM.
We outline the data modeling requirements for Culture IM derived from the German Datenraum Kultur (Culture Dataspace) project, and demonstrate how APs support application-specific knowledge representation, particularly for theater showtimes. We selected this use case as a pilot for the extensible Culture IM because our domain-expert partners—a theater association representing multiple German theaters—covered diverse scenarios and embodied the main data modeling and sharing challenges in the culture domain.
Furthermore, we explore Culture IM's conceptual integration and potential application within a dataspace architecture, demonstrating its potential for sovereign data exchange.
Culture IM provides a modular, scalable, and reusable foundation for digital cultural infrastructures.
Future work will extend its application to museum and music marketplace sectors, integrate access policy templates, and enhance tool support for non-experts.
