Ontology-Driven Semantic Alignment: Assessing the Reasoning Capabilities of Large Language Models in Geospatial Contexts

Semantic interoperability remains a critical challenge in Spatial Data Infrastructures (SDIs), particularly when aligning authoritative taxonomies with collaborative folksonomies. Recent advances in Large Language Models (LLMs) offer new avenues for automated semantic interpretation, yet these 'sub-symbolic' approaches often lack the logical rigor required for structured geospatial data. This paper evaluates the capability of LLMs – specifically distinguishing between traditional architectures and emerging Large Reasoning Models (LRMs) – to perform semantic alignment between the Brazilian national cartographic standard (EDGV) and OpenStreetMap (OSM). Using a formal ontology as a prompting scaffold, we tested seven model versions (including ChatGPT 5.0, DeepSeek R1, and Gemini 2.5) on their ability to identify semantic equivalents and generate valid ontological mappings. Results indicate that while traditional LLMs struggle with hierarchical structures, reasoning-oriented models demonstrate significantly improved capacity for complex inference, correctly identifying many-to-one (n:1) relationships across linguistic barriers. However, all models exhibited limitations in generating syntactically valid OWL code, revealing a gap between semantic comprehension and formal structuring. We conclude that a neuro-symbolic approach, using ontologies to ground AI reasoning, provides a viable pathway for semi-automated interoperability, although future work must address the lack of explicit spatial reasoning in current architectures.