Commonsense knowledge acquisition and representation is a core topic in AI, which is crucial for building more sophisticated and human-like AI systems. Existing commonsense knowledge bases organize facts in an isolated manner, like bag of facts, lacking the cognitive-level connections that humans commonly possess. People have the ability to efficiently organize vast amounts of knowledge by linking or generalizing concepts using a limited set of conceptual primitives that serve as the fundamental building blocks of reasoning. Such primitives are basic, foundational elements of thought that humans use to make sense of the world. By combining and recombining these primitives, people can construct complex ideas, solve problems, and understand new concepts. In this chapter, we describe the development of a commonsense knowledge base, termed PrimeNet, to emulate this cognitive mechanism. PrimeNet is organized in a three-layer structure: a small core of conceptual primitives (e.g., FOOD), a bigger set of concepts that connect to such primitives (e.g., fruit), and an even larger layer of entities connecting to the concepts (e.g., banana).