The ability to quickly understand scenes from visual observations via structured representations, known as Scene Graph Generation (SGG), is a crucial component of perception models. Despite recent advancements, most existing models assume perfect observations, an often-unrealistic condition in real-world scenarios. Such models can struggle with visual inputs affected by natural corruptions such as sunlight glare, extreme weather conditions, and smoke. Drawing inspiration from human hierarchical reasoning skills (i.e., from higher to lower levels) as a defense against corruption, we propose a new framework called Hierarchical Knowledge Enhanced Robust Scene Graph Generation (HiKER-SGG). First, we create a hierarchical knowledge graph, facilitating machine comprehension of this structured knowledge. Then we bridge between the constructed graph and the initial scene graph and perform message passing for hierarchical graph reasoning. Finally, we propose a hierarchical prediction head to enable the model to predict from a higher to lower level, thus enhancing robustness against corruptions that frequently impact only fine-grained details. Experiments on various settings confirm the superior performance of the proposed framework with both clean and corrupted images.