Bridging the Provenance Knowledge Gap Between 3D Digitization and Semantic Interpretation

In Notre-Dame de Paris’ digital twin, the massive data is characterized by its variability in terms of production and documentation. The question of provenance appears as the missing link in digital heritage data and a fortiori in the provenance of knowledge. The problem can be formulated as follows: the heterogeneity of data means variability as multi-device, multitemporal, multiscalar, with spatial granularity, and multi-layered and semantic complexity. The objective of this article is to improve the quality and consistency of paradata and to bridge the practical gap between mass 3D digitization and mass data enrichment in the data lineage of cultural heritage digital collections. FAIR principles, provenance, and context are keys in the data management workflows. We propose an innovative solution to integrate provenance and context seamlessly into these workflows, enabling more cohesive and reliable data enrichment. In this article, we use both conceptual modeling and quick prototyping: we posit that existing conceptual models can be used as complementary modules to document the provenance and context of research activity metadata. We focus on three models, namely the W7, the PROV ontology, and the CIDOC CRM. These models express different aspects of data and knowledge provenance. The use case from Notre-Dame de Paris’ research demonstrates the validity of the proposed hybrid modular conceptual modeling to dynamically manage the Provenance Level of Detail in cultural heritage data.