Semantic foundations for digital twins: the contribution of ontological analysis

Digital Twins (DTs) are revolutionizing industries by enabling real-time simulations, data-driven decision-making, and enhanced operational efficiency. However, their integration and scalability remain challenging due to the complexity of multi-domain systems, heterogeneous data sources, and semantic inconsistencies. This paper proposes an ontology-driven DT framework that leverages Web Ontology Language (OWL) and Description Logic (DL) to enhance semantic reasoning, data representation, and facilitates system interoperability through standards-aligned semantic mapping. A distributed ontology architecture ensures scalability and adaptability across diverse industrial applications. The results demonstrate a 60% reduction in integration time, a 75% decrease in error rates, and improved decision-making accuracy, highlighting the superiority of the ontology-based approach over traditional DTs. Comparative analysis underscores its effectiveness in addressing interoperability, semantic ambiguity, and system maintenance challenges. The findings emphasize the critical role of ontological analysis in developing self-adaptive, cross-domain DT systems. Future research will explore automated ontology generation, AI-driven semantic reasoning, and user-centric design to further enhance ontology-powered DT ecosystems.