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Spatio-temporal challenges within geospatial applications need consideration of all aspects of spatial, temporal, and semantic properties of the information (Claramunt, 2020; Jaziri et al., 2013). In fact, with the increasing size and diversity of geospatial datasets, it has become difficult for traditional geographic information system (GIS) systems to process and reason about such data (Usmani et al., 2020). It is well known that the basic functionalities of GIS are largely based on relational database management systems. Structured Query Language (SQL), widely regarded as the main tool for this task, is able to store and process structured geospatial data efficiently (International Organization for Standardization, 2023). However, although SQL is very efficient in managing large structured datasets, it is less effective in handling semantics and implicit knowledge. Most modern geospatial applications utilize data that are semantically rich and usually unstructured, with presentations in heterogeneous formats. Advanced knowledge inference capabilities responding to such challenges are therefore much needed for the interoperability among diverse datasets.
One effective approach to enhancing geospatial data is through the use of semantic technologies (Liang & Zhang, 2025, Ranatunga et al., 2025). Tools like the Resource Description Framework (RDF) and the Web Ontology Language (OWL) provide additional context and deeper meaning to data. As for RDF, it represents data as triples, thereby facilitating the combination of data coming from heterogeneous sources (World Wide Web Consortium [W3C], 2004). Meanwhile, OWL is a powerful language for defining complex ontologies. Through reasoning mechanisms, this standard enables to deduce new relationships and insights from existing data (W3Ca, 2012). Turning to SPARQL, this RDF-based query language has capabilities that go beyond the limitations of SQL. In fact, it allows extracting semantic relationships while supporting advanced queries (W3C, 2024).
Furthermore, the semantic web stack, comprising technologies such as RDF, OWL, Resource Description Framework Schema (RDFS), and SPARQL, forms the backbone of intelligent web-based systems. These technologies support formal knowledge representation, automated reasoning, and semantic interoperability, all of which are essential for achieving integration across diverse geospatial data sources. Recent advancements in Shapes Constraint Language and SPARQL 1.1 further allow validation and manipulation of semantic data with high precision, making them increasingly important in GIS applications (Buil-Aranda et al., 2013; Omran et al., 2022).
However, the underutilization of these technologies in GIS applications often is unnoticed (Kuo & Chou, 2023). Current systems struggle to effectively combine relational and semantic components, resulting in poor performance and limited capabilities for executing complex queries (Li et al., 2024; Rowland et al., 2020). To address these challenges, we introduced the Well-Architected Semantic GEOframework (WasGeo). WasGeo merges SQL functionality with SPARQL querying capabilities and OWL representation power. It operates on a three-layer architecture that integrates relational databases with semantic queries and ontology-driven reasoning, enhancing the management of geospatial data. The system was designed to execute sophisticated queries that encompass spatial relationships, semantic classifications, and temporal dynamics. WasGeo maintains this capability through efficient data transfer and seamless communication across its multiple layers. By integrating core semantic web components into its architecture, we aimed to have WasGeo bridge the long-standing gap between structured GIS operations and semantic-level reasoning, aligning the system with current trends in knowledge-driven geospatial intelligence.