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Digital twins (DT) in manufacturing, healthcare, and across different industrial domains are often over-simplified as solely a virtual representation of a physical object or service. Such a definition constitutes a dilemma in distinguishing DTs from digital models, digital shadows, digital threads, and cyber-physical systems. In this article, we aim to elucidate the concept of digital twins and its definition. Therefore, we go through the connotation of digital twins, which has its roots in space exploration and product-life cycle management, and describe the four evolution stages of DT developments. This article employs an ontological approach to clearly and comprehensively define digital twins and related key concepts, including digital models, assets, prototypes, shadows, and threads. Additionally, it presents a structured framework detailing the meta-model and the reference-level ontology of digital twins. To evaluate the proposed structure, definitions, and its important entities, we have examined our framework against 73 peer-reviewed papers in the healthcare sector from 2018 until July 2024. The evaluation and classification criteria of the selected works were based on four research questions. These criteria are driven by the core definitions provided by the main cross-domain digital twin researchers and this article’s proposed anatomy of digital twins.

Ontologies are logical theories that are used in computer science for describing different items such as web services, agents in multi-agent systems, or domain knowledge. Many ontologies exist, expressing various domains of knowledge with different abstraction levels (domain ontologies, top-level ontologies, and task ontologies are the usual categories). The conceptualization of the knowledge contained in an ontology is subject to change, whether because the context of its use changes, because the domain evolves, or because an ontology needs to interoperate with other elements using other ontologies. Change in logical theories is a form of defeasible reasoning, in which some formulas need to be added or removed from a knowledge base. Adaptive Logics (AL) is a logic managing defeasible reasoning that we investigate in this paper for managing change in ontologies expressed with Description Logics (DL). The adaptation of AL for DL will help express the context in which formulas remain valid or can be added to a DL knowledge base, and ease the interoperability between ontologies.