Nicola Carboni

As ontologies grow in complexity and breadth, domain experts often struggle to understand their intricacies without the assistance of semantic experts. As a result, the ontologies themselves can become significant obstacles in constructing reusable and consistent knowledge graphs of their data following these standards. To address these challenges, this contribution presents the Semantic Reference Data Model (SRDM) modelling method, a protocol that offers a practical approach to (i) simplifying ontological complexity, (ii) standardising semantic patterns and (iii) facilitating the creation of new knowledge graphs. SRDM provides a solution for overcoming conceptual challenges by offering a catalogue of entity-based templates, whereby recognisable entities within a domain are documented in single templates composed of ready-made and distinct ontological patterns covering the entity’s most documented attributes. The limited number of elements available, the use of domain-specific language, and the correspondence between documented objects and real-world items make the SRDMs particularly easy to grasp, helping bridge the gap between semantic and domain experts. A case study is used to illustrate the framework’s application in the cultural sector, specifically highlighting the advantages obtained in documentation, data consistency, and external data ingestion. The case study demonstrates how SRDMs can vastly simplify the creation and management of knowledge graphs, helping Digital Humanities and Cultural Heritage communities easily share essential and useful datasets.

The paper tackles the challenges of modeling the temporal validity of artist-recorded locations documented in exhibition catalogs by proposing three different ontological patterns for recording spatiotemporal-constrained assertions in a Knowledge Graph. Exhibition catalogs often document the address used by an artist participating in exhibitions. The analysis of this information offers significant insights into the spatial and social dimensions of artistic communities. However, the time-bound nature of the information makes its modeling particularly challenging, as it requires a framework that can effectively represent both the spatial and temporal dimensions of the relation. The connection between artist and their address, in fact, is always a fluent, a relationships that only hold within specific time intervals. To overcome this challenge, the study proposes and compares three ontological patterns designed for formalizing the dynamic aspects of spatial, temporal, and conceptual characteristics of an entity. The three approaches, based on RDF-star, CIDOC-CRM, and 4D ontology, are evaluated based on their ability to accurately represent the diachronic nature of addresses, formalizing (whether implicitly or explicitly) the validity of spatiotemporal assertions, and facilitating data integration and analysis.

Exhibitions play a crucial role in shaping art history by defining artistic movements and promoting visual canons. However, current models fail to capture their complex dynamics, especially in terms of contingency and participation. This study proposes a framework for modeling catalog-derived and database-derived exhibition data, by employing a bottom-up approach based on two major datasets: the Artl@s BasArt project (catalog-derived) and the MoMA Exhibition Index (database-derived). Developed using CIDOC-CRM, a de-facto standard ontology in the heritage domain, the model specifies ontological patterns for documenting key aspects of exhibitions, such as their temporal duration, spatial extension, mereological structures, source of knowledge, the role of its participants and the function of the artwork exposed. The adoption of the proposed model facilitates the integration and analysis of diverse exhibition data, enabling a comprehensive and richer understanding of the spatial, temporal, and participatory dimensions of each exhibition, helping to contextualize their reach and impact within the global artistic milieu, and enabling better data-driven studies in digital art history and cultural analytics.

Building models for integrating the diverse data generated in Cultural Heritage disciplines is a long-term challenge both for securing presently generated knowledge and for making it progressively more widely accessible and interoperable into the future. This chapter reviews the multiple approaches undertaken to address this problem, finally proposing CIDOC CRM as the most robust solution for information integration in CH. The chapter begins by outlining the data challenge specific to the field and the main approaches that can be taken in facing it. Within this frame, it distinguishes knowledge engineering and formal ontology from other information modelling techniques as the necessary approach for tackling the broader data integration problem. It then outlines the basic principles of CIDOC CRM, the ISO standard formal ontology for CH. From there, an overview is given of some of the work that has been done both theoretically and in practice over the past five years in developing and implementing CRM as a practical data integration strategy in CH, particularly looking at model extensions to handle knowledge provenance across various disciplines and typical documentation and reasoning activities, as well as at successful implementation projects. Lastly, it summarizes the present potentials and challenges for using CIDOC CRM for solving the CH data management and integration puzzle. The intended audience of this chapter are specialists from all backgrounds within the broader domain of CH with an interest in data integration and CIDOC CRM.