Semantic modelling of AI-integrated digital twins for enhanced transparency

Digital Twins (DTs) are increasingly integrating artificial intelligence (AI) to enhance predictive and adaptive capabilities, giving rise to AI-integrated Digital Twins (AI-DTs). While existing studies demonstrate the effectiveness of AI techniques within specific DT applications, they often lack an explicit system-level conceptualisation, leaving information flows, model interactions, and data transformations implicit. This limits transparency, explainability, and the informed use of AI-driven outputs. Meanwhile, information modelling approaches for conventional DTs offer limited support for representing computational models—including AI models—and their interactions with one another and with physical systems. This paper addresses these gaps by characterising information flows in AI-DTs, identifying key information modelling requirements, and proposing a generic semantic model that represents physical systems, computational models, data artefacts, and their interrelationships. By making data provenance, model dependencies, and transformation processes explicit, the proposed model supports transparency and facilitates explainability of AI-DT workflows. The feasibility of the model is demonstrated through extension and evaluation in a robot arm use case, illustrating its capability to represent and query these relationships in an integrated manner.