ontology for nature-based solutions

This thesis proposes an ontology-based framework to model NBS, providing a structured and interoperable representation of key concepts, relationships, and attributes. To achieve this, the NeOn Methodology was adopted, particularly Scenario 2 (Reusing and Reengineering Non-Ontological Resources) and Scenario 6 (Reusing, Merging, and Reengineering Ontological Resources). This approach allowed for the integration of knowledge from existing environmental ontologies such as ENVO and AGRO, as well as structured datasets from the GeoIKP platform and OPERANDUM project. The ontology development process included defining a class hierarchy, object properties, data properties, and annotation strategies, ensuring usability for both domain experts and ontology engineers. The resulting ontology was evaluated through reasoning validation, SPARQL queries, and a case study on NBS interventions for flood risk mitigation. Results demonstrated that the ontology facilitates semantic queries, knowledge discovery, and interoperability with external ontologies and datasets. Additionally, its structured nature enhances decision-support systems, environmental planning, and academic research. This work contributes to the standardization and accessibility of NBS knowledge, bridging the gap between environmental science and semantic web technologies. Future research could expand the ontology with geospatial data, AI-driven analysis, and real-world validation, further improving its applicability in environmental informatics.