Modeling and Reasoning about Incomplete, Uncertain, and Approximate Historical Dates [article]

Ryan Shaw
Extended abstract for Graphs and Networks in the Humanities 2022: Technologies, Models, Analyses, and Visualizations, 6th International Conference, 3–4 February 2022, Online. Projects working with graph technologies have the option of modeling incomplete, uncertain, and approximate historical dates using an ontology that defines concepts for temporal entities and relations, such as the Time Ontology in OWL (OWL-Time) or the CIDOC Conceptual Reference Model (Bekiari et al., 2021; Cox and Little,
more » ... 2020). Doing so makes it possible to query over incomplete, uncertain, and approximate historical dates using SPARQL and to infer possible temporal sequences using reasoners. But temporal ontologies can be dauntingly complex even for experienced data modelers, and as there are a number of possible ways to model incomplete, uncertain, and approximate historical dates, there is no guarantee of interoperability. The EDTF Ontology project provides practical tools for computationally managing historical dates by bridging the gap between EDTF expressions and ontology-driven temporal modeling and reasoning. The project has developed a OWL-Time-compatible OWL ontology for modeling EDTF concepts in a standard way (Shaw, 2021b) and a set of rules in Notation3 (Arndt et al., 2021) for automatically inferring graphs of temporal concepts and relations from EDTF expressions (Shaw, 2021a). This extended abstract explains and demonstrates the modeling constructs, focusing on the handling of incomplete, uncertain, and approximate dates.
doi:10.6084/m9.figshare.19948994.v1 fatcat:6hgo2w533bgw3e4kajl35db6ee