Entity SEO: build your entity in the Knowledge Graph

In short: a keyword is a string; an entity is a thing. Search and AI answer engines stopped matching characters years ago — they resolve a query to entities in a knowledge graph, then choose sources that are themselves recognized entities. Ranking for a keyword gets you a blue link; being a known entity gets you cited inside the answer. This is how you become an answerable node: one consistent name, verifiable sameAs links, and facts that never contradict each other across the web.

Keywords match strings; entities resolve things

Since Google’s 2012 “things, not strings” shift, the index has a second layer under the text: a graph of entities — people, companies, products, places — each with a stable identifier and a set of facts. When someone (or an LLM) asks a question, the first thing that happens is entity resolution: the query “best eSIM for Japan” is mapped to the entity eSIM, the place Japan, and an intent. The engine then looks for sources connected to those entities.

A keyword page hopes to match the words in a query. An entity is the thing the query is about. That’s the whole gap: triples build a fact, and a cluster of consistent facts about one subject builds an entity the graph can name.

Why an entity beats a keyword for citation

Axis	Keyword target	Entity
Unit	a string of characters	a thing with an identity
Matched by	lexical / vector similarity	resolution to a graph node
Ambiguity	”apple” = fruit or company?	disambiguated by ID + sameAs
Payoff	a ranked link	a citation inside the answer
Durability	swings with each algo update	compounds as facts corroborate
Portability	one engine’s SERP	every engine that reads the graph

An LLM composing an answer doesn’t reward the page that repeated a phrase most; it reaches for sources it can attribute — named, recognizable entities it has seen described consistently. Being that entity is more durable than any keyword ranking, because it compounds: every corroborating mention strengthens the node instead of resetting with the next update.

Become an answerable node

Four moves, in order of leverage.

1. One name, everywhere

Pick a single canonical name and use it verbatim — same spelling, same casing, same legal form — on your site, your profiles, your press, and your schema. “Acme”, “Acme Inc.”, and “ACME Technologies” read as three weakly-connected candidates, not one strong entity. Consistency is what lets the graph collapse the mentions into a single node.

2. sameAs to the hubs

The sameAs property is how you tell every engine “these accounts are all me.” Point your Organization or Person schema at the authority hubs — Wikidata, Wikipedia (if you have an article), LinkedIn, Crunchbase, GitHub, official social profiles. Wikidata matters most: it’s the open reconciliation hub the others cross-reference. A cluster of sameAs links is the strongest disambiguation signal you can ship yourself.

"sameAs": [
  "https://www.wikidata.org/wiki/Q…",
  "https://en.wikipedia.org/wiki/…",
  "https://www.linkedin.com/company/…"
]

3. Entity-consistent facts

The graph trusts facts that agree with themselves. Your founding year, founders, HQ, category, and product names must read identically on your About page, your schema, your Wikidata item, and your LinkedIn. One contradiction — a different founding date on Crunchbase — and the engine lowers confidence in all your facts. Pick the canonical value once and propagate it.

4. Disambiguation

If your name collides with something better-known (a common word, another brand), help the engine tell you apart: a precise description, a specific @type, category and location facts, and a distinct Wikidata item. The clearer your disambiguating facts, the less likely an answer engine attributes your query to the other “apple.”

How AI answers resolve an entity

Walk the pipeline an answer engine runs before it writes a word:

• Parse the query into candidate entities and intent.
• Resolve each candidate to a graph node — or fail, and treat it as a plain string.
• Retrieve passages connected to the resolved entities.
• Attribute — prefer sources it can name as recognized entities, because a named source is a safer citation.
• Compose the answer, citing the ones it trusts.

Notice where a keyword-only page falls out: at resolve. If nothing links your page to a named node, you compete on lexical similarity alone — and you’re invisible the moment the engine reasons about the thing instead of the words.

Checklist

• One canonical name used verbatim across site, schema, and every profile.
• Organization/Person schema with a sameAs array to Wikidata, Wikipedia, LinkedIn, and official profiles.
• A Wikidata item exists and cross-links back to your site.
• Founding year, founders, HQ, and category identical everywhere — no contradictions.
• A precise description + specific @type for disambiguation.
• Key facts expressed as clean subject-predicate-object triples.
• External mentions describe you the same way your schema does.

Run GEO checker to see which of your pages the graph can already resolve to a named entity — and which ones read as an anonymous string.

Resolves to a named entity 100%

Matches keyword only 0%

источник: internal audits, 2026

The chart is the argument in miniature: a page tied to a recognized entity survives resolution and reaches the citation step; a keyword-only page drops out before the engine ever considers it as a source.

In short

• A keyword is a string to match; an entity is a thing the graph can name.
• AI answers resolve a query to entities first, then cite recognized-entity sources.
• Build your entity with one consistent name and sameAs links to Wikidata, Wikipedia, and LinkedIn.
• Keep every fact identical across the web — one contradiction lowers confidence in all of them.
• An entity compounds and travels across engines; a keyword ranking resets with each update.

Sources

• Google, “Introducing the Knowledge Graph: things, not strings” (2012) — the shift from string-matching to entity-based understanding.
• Schema.org — sameAs — the property that binds your profiles into one entity.
• Wikidata — the open reconciliation hub other knowledge bases cross-reference for entity identity.
• Google Knowledge Graph Search API — how entities are queried by machine-readable ID.
• Aggarwal et al., “GEO”, KDD ‘24 — arXiv:2311.09735: citation depends on a source being an attributable, extractable entity.