  The ongoing digitization of the industrial sector has reached a pivotal
juncture with the emergence of Digital Twins, offering a digital representation
of physical assets and processes. One key aspect of those digital
representations are simulation models, enabling a deeper insight in the assets
current state and its characteristics. This paper asserts that the next
evolutionary step in this digitization journey involves the integration of
intelligent linkages between diverse simulation models within the Digital Twin
framework. Crucially, for the Digital Twin to be a scalable and cost-effective
solution, there is a pressing need for automated adaption, (re-)configuration,
and generation of simulation models. Recognizing the inherent challenges in
achieving such automation, this paper analyses the utilization of knowledge
graphs as a potentially very suitable technological solution. Knowledge graphs,
acting as interconnected and interrelated databases, provide a means of
seamlessly integrating different data sources, facilitating the efficient
integration and automated adaption of data and (simulation) models in the
Digital Twin. We conducted a comprehensive literature review to analyze the
current landscape of knowledge graphs in the context of Digital Twins with
focus on simulation models. By addressing the challenges associated with
scalability and maintenance, this research contributes to the effective
adaption of Digital Twins in the industrial sector, paving the way for enhanced
efficiency, adaptability, and resilience in the face of evolving technological
landscapes.
