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.