Glossary
Knowledge Graph
A knowledge graph is a structured network of entities and the relationships between them — company to founder, product to category, brand to competitor. Search systems use graphs to answer questions about things rather than text strings, and AI answers inherit those relationships when describing a brand.
In plain terms
It is a map of how you connect to everything else: your people, your products, your category, your rivals.
Why it matters
Comparison answers are generated from relationships. If the graph places you next to the wrong set of competitors, you will be listed in the wrong comparisons and omitted from the right ones.
How to apply it
- Publish and link the relationships you want understood: team, products, categories, customers.
- Use consistent identifiers across your profiles.
- Describe your category the way buyers describe it, not with an invented label.
Example
A search engine's knowledge graph links a mid-sized accounting-software company to its two direct competitors, its acquisition by a larger firm, and its category as "small business accounting software" — relationships an AI engine draws on when a user asks it to compare vendors in that category.
Related terms
Back to the full glossary (75 terms).
Ranking is no longer enough
You need to be cited, mentioned, and recommended.
Being cited, mentioned, and recommended are three different outcomes, and most brands only ever achieve the first one. Ranking is no longer enough because AI engines answer buyers directly and name only a short list of vendors as the recommendation — everyone else is cited in passing, if at all. Get a free AI Visibility Report to see exactly where your brand appears today across ChatGPT, Google AI Overviews, Gemini, Perplexity, and Copilot, where competitors are winning the recommendation instead, and what's keeping you from moving up the shortlist.