• CRMgeo
• GeoSPARQL
• geospatial narratives
• knowledge graph
• semantic web

Geospatial knowledge is fundamental to understanding and communicating narratives across historical, literary, and journalistic domains. This thesis addresses the challenge of semantic modeling for geospatial narratives within knowledge representation and the Semantic Web. It develops the Geospatial Narrative Ontology (NOnt+S), an extension of the Narrative Ontology (NOnt) based on the CIDOC Conceptual Reference Model (CIDOC CRM). NOnt+S models geospatial narratives as networks of interconnected events, enhancing expressiveness, interoperability, and data integration. Implemented in OWL 2 DL, it ensures decidability, enables semantic reasoning, and supports advanced geospatial querying and analysis.

To improve visualization, the research introduces Story Maps, interactive tools that integrate geospatial data with narrative elements, fostering engagement and comprehension. The study follows a structured methodology, including specification, conceptualization, formalization, implementation, and evaluation through case studies in the MOVING (H2020-2024) and IMAGO (PRIN 2020-2024) projects.

The findings position NOnt+S as a flexible framework that bridges narrative and spatial dimensions. By supporting semantic reasoning and aligning with Linked Open Data principles, it enhances data integration, enables complex queries, and fosters interdisciplinary collaboration. The ontology has broad applications in digital humanities, education, and various other fields, contributing to the advancement of geospatial narrative representation.