Entity-Based / Semantic SEO 2026 Skill

<objective> Covers entity-based/semantic SEO 2026: building an entity map (person/concept/organization/product) that inventories every entity a topic should cover, understanding NLP salience scoring (0-1, relative — entities on a page compete for a total that sums to ~1.0) and passage-level ranking, and wiring `about` (Wikidata URI), `sameAs`, and `knowsAbout` schema to resolve identity against the Knowledge Graph (~8 billion entities). Includes the Google Cloud Natural Language API validation step to confirm the primary entity wins salience before publishing. Distinct from seo-schema (general JSON-LD types/templates) and seo-content (topical coverage, answer capsules). </objective>

Entity-Based / Semantic SEO 2026

Google parses meaning at query, document and passage level. It extracts entities via NLP, resolves them against the Knowledge Graph (~8 billion entities), maps relationships, and indexes content by concept, not by keyword. TF-IDF / keyword density is obsolete — optimize for embeddings and topical coverage instead.

Entity Map (start here)

Strategic inventory that drives everything else. For the target topic, list every entity the site should cover:

| Entity | Type | Relates to | Page covering it | |--------|------|-----------|------------------| | RankBrain | concept | Google, ranking | /guides/rankbrain | | Google | organization | search engine | /about |

Types: person, concept, organization, product. Breadth of entity coverage + internal linking density + publishing consistency = topical authority.

Salience (0–1, relative)

NLP scores each entity's prominence; scores across all entities on a page sum to ~1.0 — entities compete for share.
Entity in the H1 + first 100 words = max salience.
Repetition does NOT raise salience. Clear writing in proper context does.
Example: a passage can score RankBrain 0.584 vs Google 0.231 even when "Google" is the grammatical subject — salience distribution reveals the real topical focus.

Passage-Level Ranking

Google scores per passage, not per page. Each passage is judged on entity salience, relationship clarity, and topical relevance. A page ranks for broad topic queries when its entity signals are clear and unambiguous — not just for the exact keyword.

Schema Linking (resolve identity, don't make Google guess)

about → Wikidata URI of the page's primary entity (the node in the Knowledge Graph).
sameAs on author/organization → LinkedIn, Wikipedia, Wikidata profiles.
knowsAbout on author/organization → entities they have demonstrated expertise in.

{
  "@type": "Article",
  "about": { "@type": "Thing", "name": "Knowledge Graph",
    "sameAs": "https://www.wikidata.org/wiki/Q3882486" },
  "author": { "@type": "Person", "name": "Jane Doe",
    "sameAs": ["https://www.linkedin.com/in/janedoe",
      "https://en.wikipedia.org/wiki/Jane_Doe"],
    "knowsAbout": ["semantic SEO", "NLP"] }
}

Validation (before publishing)

Send content to the Google Cloud Natural Language API (free tier). It returns identified entities, types, salience scores, and Knowledge Graph links. Use it to confirm the primary entity actually wins salience; rewrite passages if salience drifts off-topic.

seo-schema — JSON-LD types and templates
seo-geo — entity signals drive AI citations
seo-content — topical coverage and answer capsules

Agent Skills: Entity-Based / Semantic SEO 2026

Install this agent skill to your local

Skill Files