Knowledge Graph
Why it matters
Search and AI engines describe a business from what they can connect and trust. A company that is clearly mapped, with its product, founder and category linked across reliable sources, gives an engine facts to stand on. One that is not gets summarised vaguely, placed in the wrong category, or skipped in favour of a competitor the engine can describe with confidence. For AI answers, this is the difference between being named correctly and not being named at all.
How to apply it
- Use one name and one category for the business everywhere it appears, so engines do not have to guess which entity is meant.
- Add structured data to the site, so each page states in a machine-readable form what it is about.
- Earn mentions on sources engines already trust, as well as on the company's own site.
- Keep public facts consistent: the founder's name, the category, the location, the founding date.
- Treat a correction as unfinished until every other public listing agrees with it.
What it is
A knowledge graph stores facts as connections. A node is a thing: a company, a founder, a product. An edge is a relationship between two things: "founded by", "makes", "is located in". Google introduced its own Knowledge Graph in 2012, and it powers the information panels shown beside many search results. The idea also works inside a company, where a private graph links customers, deals, documents and decisions so that software can answer questions across them.
Common mistakes
- Describing the business differently on the site, in directories and in old press mentions.
- Assuming a graph entry appears automatically. Entries need consistent, verifiable sources behind them.