A taxonomy provides hierarchical classification (e.g., Products > Electronics > Laptops), whilst a knowledge graph represents entities and their multi-dimensional relationships. Taxonomies answer "what category?" whilst knowledge graphs answer "what is this, how does it relate to other things, and what are its properties?" A laptop in a knowledge graph might connect to its manufacturer (Organization), sellers (LocalBusiness), reviews (Review), specifications (PropertyValue), related accessories (Product), and purchasing options (Offer). Knowledge graphs enable AI agents to traverse these relationships, understanding context that flat taxonomies cannot express. VISEON builds knowledge graphs using Schema.org vocabulary, creating rich semantic networks that AI systems can reason over for discovery, comparison, and recommendation tasks that power agentic commerce.
