Geospatial knowledge cube: enhancing semantic query in geospatial data cubes with ontology and LLMs

Geospatial Data Cubes (GDCs) raise as a new foundational infrastructure for managing multidimensional heterogeneous spatial datasets. Existing GDC efforts mainly focus on improving computational efficiency, ignoring the optimization of heterogeneous data query due to the lack of semantic connections between them. In this paper, we propose Geospatial Knowledge Cube (GKC) to address the above problem. First, we build a semantic description layer grounded in ontology languages and align it with the schema of GDC. In this way, entities, relationships, and attributes are modeled to enable semantic mappings between heterogeneous datasets. Second, we combine large language models (LLMs) with ontology to transfer natural language queries into machine-readable queries, which significantly improves query efficiency and applicability. Experimental results demonstrate that our method achieves 28.4% higher accuracy than text-to-SQL methods on complex factual queries, 36.3% greater precision for analytical queries, and 22.9% overall accuracy improvement with significantly reduced error rates and enhanced robustness. Furthermore, we applied the GKC framework to flood warning scenarios in the Rhine–Meuse Delta in the case study, which validates the capabilities of GKC.