"Cognitive Digital Twins for smart lifecycle management of built environment and infrastructure focuses on the challenges and opportunities of data-driven cognitive systems. The text integrates the heterogeneous data from multiple resources that can easily be used in a machine learning model and adjusted with algorithms. Digital Twins incorporate cognitive features that will enable sensing complex and unpredicted behavior, and dynamic strategies for process optimization to support decision-making in lifecycle management of the built environment and infrastructure. This book introduces the Knowledge Graph (KG)-centric framework for Cognitive Digital Twins involving process modeling and simulation, ontology-based Knowledge Graph, analytics for process optimizations, and interfaces for data operability. This book offers contributions of Cognitive Digital Twins for the integration of IoT, Big data, AI, smart sensors, machine learning and communication technologies, all connected to a novel paradigm of self-learning hybrid models with proactive cognitive capabilities. Presents the topologies of models described for autonomous real time interpretation and decision-making support of complex system development based on Cognitive Digital Twins with applications in critical domains such as maintenance of complex engineering assets in built environment and infrastructure. Finally, the text provides the essential material to enlighten pertinent research communities of the state-of-the-art research and the latest development in the area of Cognitive Digital Twins, as well as a valuable reference for planners, designers, developers, and ICT experts who are working towards the development and implementation of autonomous Cognitive IoT based on big data analytics and context-aware computing"--