Quantities, Units, Dimensions, and Data Types (QUDT) (QUDT)

of measurement hinder effective collaboration and large-scale analysis. To address these barriers, this research introduces a novel methodology that exploits Large Language Models to semantically align two key ontologies: the Platform Material Digital Core Ontology (PMDco), which describes experimental methodologies and material measurements, and the Quantities, Units, Dimensions and Types Ontology (QUDT), which standardizes units and quantities. Our methodology systematically aligns PMDco classes such as those quantifying mass loss, corrosion depth, or corrosion rate to precise QUDT units by assessing semantic labels, structural context, and physical relevance using LLM-generated interpretations. The resulting OWL-based semantic mappings enable automated recognition and normalization of measurement data across heterogeneous sources, facilitating consistent interpretation despite variations in units or test protocols. The significance of this ontology alignment lies in its ability to substantially improve semantic interoperability within corrosion datasets, thereby reducing ambiguity, preventing misinterpretation, and enabling automated reasoning. Domain experts benefit directly from more efficient cross-laboratory data comparisons, enhanced data quality, and strengthened foundations for scalable materials informatics infrastructures. Ultimately, this approach supports FAIR (Findable, Accessible, Interoperable, Reusable) data principles, advancing digital transformation in corrosion research and material science.