Knowledge Representation (KR) using structured objects, such as semantic nets and frames, involves organizing and representing knowledge in a way that mimics human understanding. A semantic net is a graph-based structure where concepts are represented as nodes, and relationships between them are represented as edges. This approach enables the modeling of associative relationships and hierarchies between concepts, providing a way to represent facts and the context in which they hold true. Frames, on the other hand, are data structures that contain slots or attributes, each of which holds values or pointers to other frames. Frames are useful for representing stereotypical situations, allowing the encoding of more complex, object-oriented knowledge. Both structures help capture knowledge in a way that is understandable and manipulable by machines, facilitating reasoning and inference in artificial intelligence applications.