Schema Markup / Structured Data

Schema markup, or structured data, is machine-readable metadata embedded in a page, most commonly as JSON-LD, that explicitly tells crawlers what the content actually is: an article, an organization, a product, a set of questions and answers, rather than leaving that classification to inference.

Why it matters

Structured data is one of the few GEO levers confirmed to matter across nearly every AI system and traditional search alike, since it removes ambiguity a crawler would otherwise have to guess at, and it directly supports entity-level verification and knowledge graph inclusion.

Related terms

• JSON-LD

  JSON-LD is the specific structured-data format, a block of JSON embedded in a page's HTML, that both traditional search engines and AI systems prefer for schema markup over older formats like microdata, because it's easier to generate, validate, and parse without altering a page's visible layout.

• sameAs

  sameAs is a schema property that links a brand or person's identity on one page to their authoritative external profiles elsewhere, commonly Wikidata, Wikipedia, and LinkedIn, giving crawlers a direct, machine-readable way to confirm that different mentions across the web refer to the same entity.

• FAQ Schema

  FAQ schema is structured markup for question-and-answer pairs on a page, explicitly labeling each question and its corresponding answer so a crawler doesn't have to infer that structure from formatting alone. It's one of the most directly applicable schema types for content already organized around specific questions.

• Entity-Level Verification

  Entity-level verification is Gemini's practice of cross-checking a source's claims against Google's knowledge graph before citing it, confirming that the brand, person, or fact referenced is a recognized, disambiguated entity rather than an unverified mention. A claim tied to a well-established entity clears this check more easily than one tied to an ambiguous or newly formed one.