From Assets and Processes to Service Ecosystems: A Hierarchical Digital Twin Framework for Knowledge Representation
Digital twins (DTs) have become a central paradigm for modeling cyber–physical systems and digital infrastructures, yet the term is applied to very different representations—from physical assets to operational processes and service environments. This ambiguity obscures how the various DT interpretations relate to one another and at which level knowledge can be represented and extracted. This paper develops a conceptual and mathematical framework that treats asset-centric, process-centric, and service-centric DTs as successive levels of system abstraction. DTs are modeled as mappings between real-world entities and their digital representations, and the three paradigms are connected through explicit cross-layer dependencies, with service-centric twins shown to form a distinct level that cannot be reduced to asset and process descriptions alone; the framework is then extended to the ecosystem level as a digital service ecosystem twin. Because each level fixes the entities, features, and relations available to data-driven methods, the framework also specifies where machine-learning and knowledge-extraction tasks operate within layered DT architectures. The approach is illustrated and validated for structural and cross-layer consistency through a smart-city electricity ecosystem, providing a unified basis for interpreting the evolution of DTs toward service-oriented digital ecosystems.