Semantic drift evaluation in language and data-specific digital twin frameworks

• Eliciting a comprehensive of requirements to qualify and manage semantic drift in model-driven digital twins. • Developing assessment metrics, grounded in the requirements, to evaluate digital twin frameworks in their ability to address semantic drift. • Evaluating a selection of language and data specific frameworks supporting model-driven digital twins. • Reporting on the limitations of existing frameworks as evaluated by our metrics. Digital Twin (DT) technology is a key enabler of Industry 4.0, integrating diverse digital models to optimize processes, enhance decisions, and support predictive maintenance. However, as physical systems evolve, delays in synchronization can cause semantic drift, leading to discrepancies between digital and real-world entities. Effective semantic drift management needs DT frameworks that support all modeling layers, i.e., data, model, metamodel, and ontology, and provide different model management mechanisms. In this paper, we make three key contributions. First, we define a comprehensive set of requirements to address semantic drift effectively, drawing on both our understanding of the phenomenon and a real-world use case on mobility. These requirements capture the essential characteristics needed to maintain synchronization between the digital and physical domains as systems evolve. Second, we introduce a set of metrics designed to evaluate the ability of DT platforms to tackle semantic drift, grounded in both the defined requirements and insights of the use-case. Finally, we apply these metrics to evaluate four existing DT platforms demonstrating their utility for identifying the limitations of current frameworks in handling semantic drift. Through this evaluation, we highlight the strengths and weaknesses of these platforms, providing a foundation for future improvements in DT infrastructure.