Kubernetes has become the de facto standard for orchestrating containerised applications within contemporary cloud-native architectures. However, as these deployments increase in scale and complexity, managing and optimising Kubernetes clusters have become more challenging. Existing tools and methodologies predominantly focus on infrastructure management but often lack the expressive capability to capture the intricate semantic relationships among resources, configurations, and services. This paper presents a novel approach to semantic modelling specifically tailored to Kubernetes environments. It employs ontologies to facilitate a more structured and semantically enriched representation of cluster components and their interactions. The approach aims to enhance automation, improve resource allocation efficiency, and streamline troubleshooting by integrating semantic models. The proposed methodology is rigorously evaluated in terms of its efficiency, and potential to reduce operational overhead. Experimental findings indicate that an ontology-based semantic model can significantly advance the management and comprehension of Kubernetes clusters, thereby enabling more intelligent and automated practices.