The integration of ontologies within digital twinning frameworks presents a significant opportunity to elevate data management and operational capabilities beyond conventional data annotation. Ontologies, traditionally valued for enabling a FAIR (Findable, Accessible, Interoperable, and Reusable) data structure, offer a foundation for advanced data functionalities essential in complex systems such as digital twins. This work discusses how ontologies enable seamless data integration, supporting the convergence of heterogeneous datasets through standardized vocabularies and facilitating interoperability across digital platforms. Beyond data harmonization, ontologies can represent complex relationships and hierarchical structures within datasets, enhancing the precision of model inputs and outputs. Leveraging graph datasets and enabling applications like federated learning, advanced data extraction, and data standardization, ontologies transform digital twins into robust tools for comprehensive data analysis and operational intelligence. This work thus emphasizes the need to reframe ontologies as active components within digital twins
