Digital Twins (DT) is an emerging technology that helps activities in process simulation, monitoring, and control as well as develops insights on process parameter relationships to achieve high quality AEC activities. As an evolution, CDTs make use of technologies and services designed to give DTs cognitive skills similar to those of humans. On the other hand, the knowledge graph powered by cognition may be used to multi-source heterogeneous data of customized items to achieve autonomous data fusion. This chapter provides the framework for the cognitive modular production paradigm by detailing a data representation technique based on a knowledge graph that can be utilized to enable reasoning and decision-making via graph embedding. The data from the Industrial Internet of Things (IIoT) is combined using an internal knowledge tree with many layers. The cognitive module receives the device's process knowledge and analyzes it for the whole line in order to predict the device's perception and cognition actions. These reactions are made feasible through the use of perception and cognitive systems. This chapter presents an IIoTs-enabled knowledge graph-based data representation approach for cognitive modular production and focuses on how to construct a shop-floor CDT and implementation process using Model-based Systems Engineering (MBSE).