We propose a vision of EdgeKBs(Edge Knowledge Bases) integrating KBs into Edge AI systems. EdgeKBs can infuse commonsense and domain knowledge within Edge devices to guide them while learning from training data. By doing so, we not only bring explainability and performance benefits of KBs to the Edge, but also enable more efficient, sustainable Edge AI systems. We envisage three approaches to develop EdgeKBs: Slicing Dicing; Tailoring to Fit; and Dynamic EdgeKBs. These EdgeKBs will bring Knowledge-Guided Machine Learning (KGML) to the Edge to support next-generation applications, e.g., Physical AI, cobots, and local natural language interfaces.