The paper concerns the definition of a novel ontology for cultural heritage based on the concept of digital twin. The ontology, called Heritage Digital Twin ontology, is a compatible extension of the well-known CIDOC CRM ISO standard for cultural heritage documentation and incorporates all the different documentation systems presently in use for cultural heritage documentation. In the authors’ view, it supports documentation interoperability at a higher level than the ones currently in use and enables effective cooperation among different users.

The logicist program, which was initiated in the 1970s by J.C. Gardin, aims to clarify the reasoning processes in the field of archaeology and to explore new forms of publication, in order to overcome the growing imbalance between the flood of publications and our capacities of assimilation. The logicist program brings out the cognitive structure of archaeological constructs, which establishes a bridge between empirical facts or descriptive propositions, at one end of the argumentation, and interpretative propositions at the other. This alternative form of publication is designed to highlight the chain of inference and the evidence on which it stands. In the case of the logicist publication of the archaeological excavation in Rigny (Indre-et-Loire, France), our workflow can provide different levels of access to the content, allowing both speed-reading and in-depth consultation. Both the chains of inference and the ArSol database containing the field records that provide evidence for the initial propositions are visualized in a diagram structure. We rely on the International Committee for Documentation Conceptual Reference Model (CIDOC CRM) entities for ensuring the semantic interoperability of such publications within the Linked Open Data. Inference chains are mapped to CRMinf and ArSol records are mapped to CRM, CRMSci and CRMArcheo. Moreover, as part of the work carried out by the French Huma-Num MASA Consortium, a project is underway to allow the building of logicist publications starting from a graphical interface for describing the structure and content of propositions.

As more and more knowledge graphs (KGs) are published on the Web, there is a need for tools that show their content. This implies showing the schema-level patterns instantiated in the graph, but also the terms used to qualify its entities. In this article, we present a new profiling tool that we call TTprofiler. It shows the predicates that relate types in the KG, and also the terms present in this KG, because of their paramount importance in most KGs, especially in the Cultural Heritage (CH) domain. We recall the role of terminologies and how they are implemented and used on the Web, we give the algorithm for building a TT profile from an online KG’s endpoint, and we report on experiments performed over a set of Cultural Heritage Web KGs. A tool for visualizing TT profiles is also provided.

When dealing with the structure, content, and quality of knowledge graphs (KGs), most analyses focus on entities, overlooking the significance of relationships and their evolution. In this article, we introduce K nowledge REL ationship M odel (KRELM), a novel and efficient graph model that mimics the behavior of facts accumulation in crowdsourced KGs and accurately simulates the evolution of their structure. By modeling the decentralized process of crowdsourcing, KRELM reproduces key distribution patterns found in relationships, demonstrating that the facts in a KG can be generated incrementally, either by adding new entities or by further describing existing ones. Our theoretical analysis of KRELM reveals that the distribution of facts for relationships follows an exponential law for subjects and a power law for objects, enabling a deeper understanding of knowledge graph dynamics. Experimental validation on major KGs shows that KRELM successfully captures a large part of the structure of real-world relationships, and a longitudinal study of Wikidata confirms its effectiveness in predicting relationship evolution. This work opens new avenues for analyzing and benchmarking KGs.