Entity Optimization for AI Search

TL;DR.

• An entity is anything an AI system can identify as a single, distinct thing — a person, organization, product, place, concept, or event.
• AI systems cite entities they can confidently resolve. The two highest-leverage moves are claiming a Wikidata QID and publishing complete schema.org markup with sameAs to authoritative profiles.
• Use sameAs for identity (Wikidata, Wikipedia, official social profiles); use knowsAbout for expertise and topical scope.
• Maintain entity salience by mentioning the canonical entity name early, repeatedly but naturally, with attributes and relationships in plain prose.
• Validate quarterly with Schema Markup Validator, Google NLP API, and Knowledge Panel monitoring.

What is an entity?

An entity is any uniquely identifiable thing AI systems can recognise and reason about. Entities are the nodes in a knowledge graph; relationships between them are the edges. The same way classical GEO treats pages as the unit of optimisation, entity optimisation treats things as the unit — your company, your founders, your products, your defined concepts.

The most useful entity types for GEO programs are:

Why entity optimization matters for AI search

Generative search engines do not retrieve and rank pages the way classical search does. They resolve a query into entities, retrieve evidence about those entities, and synthesise an answer that cites sources whose authority can be reconciled to a knowledge graph. When an entity is:

• Well-defined and disambiguated, AI systems describe and cite it confidently.
• Ambiguous (sharing a name with another entity, missing identifiers), AI systems either confuse it with a competitor or avoid mentioning it.
• Unknown (no Wikidata entry, no schema, no authoritative profiles), AI systems cannot cite it at all.

Independent industry analyses in 2025-2026 (Adobe, Onely, Schema App, ALM Corp) consistently identify entity-level signals — not page-level keyword density — as the most transferable foundation for AI citation work.

sameAs vs knowsAbout: pick the right property

Most entity optimization mistakes come from conflating identity with expertise. Schema.org gives you two distinct properties; they are not interchangeable.

Use sameAs to reconcile identity. Use knowsAbout to declare what your entity is an authority on. Wikidata recommends URL-form values for both; knowsAbout accepts plain strings as a fallback (Schema.org V30.0, March 2026; Will Scott, July 2025).

The 6-phase entity optimization workflow

This workflow is sequenced for a typical mid-market site. Larger publishers can parallelise phases 2-3 and 4-5.

Phase 1: Inventory your entities

List every brand, person, product, and defined concept you want AI systems to know about. For each, capture: canonical name, on-site canonical URL, current Wikidata QID (if any), current Wikipedia URL (if any), and major social profile URLs. This list becomes the spine of every later phase.

Phase 2: Disambiguate and claim identifiers

For each entity, secure stable identifiers in priority order:

1. Wikidata QID. Highest leverage for AI knowledge graphs. If your entity is notable enough, create or claim the QID and populate descriptive properties (founded by, instance of, official website, country of origin).
2. Wikipedia article. Useful when an entity meets notability guidelines. Often higher cost than Wikidata; not every brand needs a Wikipedia page to perform well in AI search.
3. Canonical URL on your domain. Each major entity should have one definitive page — /about, a /team/{slug}, a /products/{slug}, or a /concepts/{slug}.
4. Authoritative social profiles. LinkedIn for organizations and people, GitHub for technical projects, Crunchbase for funded companies, etc.

Phase 3: Mark up entities with schema.org

Publish schema.org markup on each entity's canonical page. JSON-LD is the preferred encoding; embed it in a