Development of an Ontology-Based Framework to Enhance Geospatial Data Discovery and Selection in Geoportals for Natural-Hazard Early Warning Systems

Discovering and selecting relevant geospatial datasets from heterogeneous sources remains difficult in conventional geoportals, where keyword-based search often fails to capture thematic relationships or user intent. This article presents an ontology-based framework that augments geoportals with semantic-aware discovery and selection. The contributions are as follows: (1) the geospatial metadata ontology (GMO), which reuses W3C and OGC ontologies and aligns with ISO 19115 to provide a uniform metadata representation enriched with thematic hierarchies and relations; and (2) GeoFit, a discovery framework that integrates GMO into geoportal workflows. The framework extends conventional functionality by enabling semantic query expansion, faceted exploration of thematic hierarchies, and ranking of datasets according to conceptual proximity and fitness-for-use criteria. These capabilities demonstrate how ontology integration operationalizes domain knowledge in the discovery process and makes dataset selection more interpretable and targeted. Validation demonstrated feasibility in the context of natural hazard Early Warning Systems (EWSs), where the prototype surfaced datasets relevant to different components, organized them into ranked and navigable results, and illustrated portability of the method to applied settings. The study confirms that embedding an ontology layer into geoportals provides semantic capabilities absent from keyword-only interfaces and establishes a foundation for extending discovery functions in heterogeneous geospatial infrastructures.