Inspired by principles of cognitive engineering, this study explores the formalization of the results of an Abstraction Decomposition Hierarchy (ADH) towards a Digital Twin for supporting the design of interactive information systems for chemical processes. ADH is a two-dimensional hierarchical space, comprising a functional abstraction hierarchy and a physical decomposition hierarchy. While the decomposition dimension deals with the process sectioning from a superficial organizational perspective, the functional dimension analyzes the actual phenomena occurring in the process. This framework offers a formalism for presenting information in a way to enhance the decision-making and fault diagnosis abilities of human operators. In this research, we leverage OntoCAPE, a widely recognized ontology for semantically describing Computer-Aided Process Engineering (CAPE), to create a knowledge graph of the ADH. To investigate the merits and limits of the suggested formalization, we applied it to the Tennessee Eastman Process (TEP). Our model is capable of answering competency questions not only regarding the structural aspects of the process, including the configuration of process units and the connectivity between various pieces of equipment, but also the actual physico-chemical phenomena happening in the process and their influence on process parameters. This work presents promising results for further development of software tools based on an ADH-driven knowledge graph for the design of decision support systems. These tools have the potential to significantly advance decision support and fault diagnosis in complex processes.