Overview

Amazon's product discovery engine has evolved far past simple keyword matching. At its foundation sits the Knowledge Graph — a vast semantic network that connects products, brands, categories, attributes, and shopper intent into a single interconnected system. For sellers, supplying accurate structured data into this system has become one of the most powerful levers for driving visibility and conversions.

What Is Knowledge Graph Optimization?

Knowledge Graph Optimization (KGO) is the discipline of using structured data — backend attributes, product classifications, specifications, and brand details — to precisely define where a product sits within Amazon's semantic framework. Instead of chasing keyword density, KGO focuses on building clear entity definitions and relationships that Amazon's AI systems, including Rufus, can reliably interpret.

When a shopper asks Rufus something like "best wireless mouse for gaming," the AI doesn't simply scan listings for matching keywords. It navigates the Knowledge Graph to determine which products genuinely belong to the gaming mouse category, which brands are associated with gaming peripherals, and which attributes — such as DPI, response time, and ergonomic design — are most relevant. A product's structured data dictates whether it appears in that traversal or gets bypassed entirely.

The Six Pillars of Structured Data

Sellers interact with structured data through several areas in Seller Central, each feeding the Knowledge Graph in distinct ways.

Backend Search Terms and Hidden Attributes — Backend fields supply additional context without cluttering the customer-facing listing. Many sellers leave optional fields empty, but completing them provides Amazon's AI with maximum context for accurate product classification. These fields function as metadata that tells the system what a product actually is, not merely what it's called.

Product Type and Browse Node Classification — Product type and browse node placement shape how Amazon categorizes items within its taxonomy. This extends beyond visible browse categories — it determines how AI systems understand what a product is and who it serves. Items assigned to incorrect browse nodes may never surface for relevant AI-driven recommendations regardless of listing quality.