Glossary

What is Knowledge Graph?

A structured database of entities and their relationships that search engines and AI systems use to resolve who's who and what's what.

Definition

A knowledge graph is a structured map of entities — people, companies, products, concepts — and the relationships between them. Google's Knowledge Graph powers knowledge panels and entity understanding; Wikidata is the open equivalent feeding countless AI systems' understanding of the world.

When an assistant correctly says “Seeqly is an AI search visibility platform,” it's drawing on graph-style entity knowledge, learned or retrieved.

Why it matters

Graph presence is machine-verifiable identity. Brands with clean entity records get described accurately and slotted into the right comparisons; brands without them depend on whatever the model inferred from scattered text. Securing the basics — Wikidata entry, consistent Organization schema with sameAs, aligned descriptions across major profiles — is among the highest-leverage, lowest-cost GEO work available.

Frequently asked

How do I get into Google's Knowledge Graph?

Build consistent, corroborated entity signals: Organization schema, Wikidata/Wikipedia presence where merited, consistent NAP-style data across authoritative profiles, and press coverage using your canonical description.

Do LLMs use knowledge graphs directly?

Some systems retrieve from them; all models absorb graph-derived text (like Wikipedia) in training. Either way, graph consistency propagates into how models describe you.

Related terms