Structured Data for Gemini: Which Schema Types Actually Matter

Structured data matters for Gemini the way a well-labeled database matters to an analyst: it does not create the answer, but it makes your facts unambiguous to the systems that do. Gemini grounds on Google's index and Knowledge Graph, and schema.org markup — Google recommends JSON-LD — is the primary channel by which you declare entities, authorship, products, and Q&A structure to that pipeline. Implement the types below in priority order, and treat everything past them as marginal.

Schema types ranked for Gemini impact

Why Organization schema is the top priority

Gemini describes brands from entity understanding, and entity understanding starts with your Organization markup reconciled against the wider web. Declare name, url, logo, and — the underused property — sameAs links to your Wikipedia page, Wikidata item, LinkedIn, and other authoritative profiles. When those references corroborate each other, Google's Knowledge Graph resolves you into a confident entity, and Gemini's brand-direct answers ("what is [brand]?") get noticeably more accurate. Inconsistent naming across properties is the most common self-inflicted entity wound; pick one canonical name form and enforce it everywhere.

A copy-paste starting point

{
  "@context": "https://schema.org",
  "@type": "Organization",
  "name": "Example Co",
  "url": "https://example.com",
  "logo": "https://example.com/logo.png",
  "sameAs": [
    "https://www.linkedin.com/company/example-co",
    "https://www.wikidata.org/wiki/Q000000"
  ]
}

Adapt per page type: an article page carries Article referencing its author Person; a product page carries Product with nested Offer. One primary entity per page keeps the graph legible — see schema.org for full property definitions.

The August 2023 caveat, and why markup survived it

Google restricted FAQ rich results to authoritative government and health sites in August 2023 and deprecated HowTo rich results around the same time. That changed SERP decoration, not machine readability: the structured Q&A and step data still enters the index, still disambiguates your content, and still serves any system — Gemini included — that benefits from knowing where an answer starts and ends. The rule of thumb after 2023: implement schema to describe reality for machines, never to chase a rich-result pixel.

Testing and maintenance

Validate twice, with different questions. Google's Rich Results Test (search.google.com/test/rich-results) answers "does Google parse this and recognize eligible features?"; the Schema Markup Validator at validator.schema.org answers "is this valid vocabulary at all?" — the second catches types the first ignores. Then watch Search Console's enhancement and structured-data reports for regressions after template deploys, the classic silent failure. Two standing rules keep you out of trouble: schema must never claim what the visible page does not (a Google spam-policy line as well as a trust issue), and prices, dates, and ratings in markup must update in the same deploy as the page. The AEO checklist folds these checks into a repeatable audit, and the glossary covers the vocabulary — JSON-LD, Knowledge Graph, entity resolution — that this whole layer stands on.