The Cognitive Digital Twin (CDT) is an advanced version of the Digital Twin model. It integrates cognitive computing technologies to create systems that not only connect but also reason, learn from past experiences, and make informed decisions. Integrating machine learning algorithms and artificial intelligence allows CDTs to process and interpret data. This cognitive capability enables the digital twin to function with a layer of intelligence that mimics human cognitive abilities, making the system adaptable to its environment and capable of handling complex decision-making processes autonomously. The cognitive features of CDTs are crucial as they enable the system to predict future states, identify potential problems before they occur, and suggest mitigating actions. Furthermore, semantic web technologies can facilitate advanced analytics and machine learning within CDTs. This article offers a rapid analysis of how Semantic Web approaches can support several aspects of CDT models.