Knowledge Graph
A knowledge graph is a structured database of entities and the relationships between them, used by search and AI systems to reason about the world rather than just match text.
Google's Knowledge Graph is the best known, powering the information panels beside search results. Similar structures inform how AI systems understand which companies exist, what they do and how they relate.
Presence in such a graph is earned through consistent, corroborated signals across many sources — not by asking. Structured data and third-party profiles feed it.
Why it matters
A business represented in a knowledge graph is far easier for any system to recommend confidently, because its identity and attributes are already established rather than inferred at query time.
An example
Search for a well-known company and a panel appears beside the results with its logo, founding date, headquarters and leadership. Those facts were not scraped from one page at query time — they come from a graph that already holds the company as an entity with known attributes, assembled from many sources over time.
Common mistakes
- Expecting to be added on request. Presence is earned through consistent, corroborated signals.
- Assuming structured data alone is sufficient. It feeds the graph, but independent sources are what confirm it.
Questions
- How do I get into Google's Knowledge Graph?
- There is no application. Consistent structured data, a clear About page, and matching facts across authoritative third-party sources are what feed it. Once an entity is recognised, a verified representative can claim and suggest edits to its knowledge panel.
- Do AI assistants use knowledge graphs?
- Many draw on structured knowledge about entities alongside what they retrieve. A business whose identity is already well established is easier for any system to describe and recommend with confidence.