An ontology-centered approach to climate loss data integration and analytics

Abstract

Climate adaptation planning and risk transfer mechanisms rely on accurate, interoperable loss data. However, integrating heterogeneous datasets from insurers, public authorities and academic organizations remains a major challenge due to inconsistent formats, definitions, and privacy constraints. This study proposes a semantic approach to harmonize and operationalize climate-related economic loss data. We introduce the SOTERIA Ontology, designed to standardize key entities, such as assets, hazards, damages, and claims, thereby enabling semantic interoperability across diverse sources. Using Norwegian address-level insurance data, we populated a knowledge graph and developed a web-based prototype for data visualization and exploration. The prototype supports granular queries, interactive visualization, and automated Risk Data Hub (RDH) compliant aggregation. The results obtained through the prototype highlight clear spatial, and exposure-related patterns in loss distribution. Flood damages were concentrated in low-lying riverine municipalities, while storm damages were more prevalent in exposed coastal regions. The likelihood of flood damage decreased with increasing distance from mapped flood hazard areas, storm damage rates increased with wind exposure, and average flood damage costs rose with building footprint size. These results demonstrate the potential of ontology-driven systems to enhance data quality, enable advanced analytics, and support evidence-based climate adaptation strategies. This work delivers the first ontology-based framework and prototype specifically designed to integrate insurance-based economic loss data into European risk governance workflows aligned with the RDH. • We present an ontology-driven approach to integrate heterogeneous climate-related economic loss data for improved interoperability. • The SOTERIA Ontology and knowledge graph enable advanced querying, visualization, and Risk Data Hub–aligned aggregation. • A Norwegian case study demonstrates the feasibility of semantic integration using detailed insurance claims and exposure datasets. • Our framework enhances data quality and supports evidence-based climate adaptation strategies and risk-informed decision-making.