Schema Markup (Schema.org)

Schema markup is code added to a web page that describes its content using the Schema.org vocabulary. It tells machines, in a fixed format, that this page is an Article written by a named Person, or a Product with a price and a return policy, or an Organisation with a logo and official profiles. It is the most common way of adding structured data to a site.

How it works

Schema.org was launched in 2011 by Google, Microsoft and Yahoo (Yandex joined later) as a shared vocabulary of types and properties. A type such as Organization, Product, Event or Person has properties such as name, url, founder or offers. You express these in one of three formats, and Google recommends JSON-LD: a block of script in the page that sits separately from the visible HTML.

Well-built markup is more than a list of fields. With @id identifiers, each entity (your organisation, an author, a product) becomes a node that other pages can reference, so the markup across a site forms a connected entity graph. The sameAs property links those nodes to external profiles such as Wikidata or LinkedIn.

How it’s used in SEO and marketing

• Eligibility for rich results, such as product, review, event or recipe enhancements. Google has retired many types over the years (HowTo, and in 2025–2026 several more), so check the current gallery before investing.
• Entity understanding: Organisation, Person and Product markup reduces ambiguity about who you are and what you sell.
• Agent readiness: MLforSEO’s agentic SEO material treats schema as the primary document for AI agents, because fields such as price, availability or a merchant return policy can be read without inference. A missing required field can remove a candidate entirely.
• Scaling: generating and validating JSON-LD from your own data with Python, rather than by hand.

Example

An accountancy firm adds Organization markup with its legal name, logo, address, areaServed, knowsLanguage, a contactPoint with a booking URL, and sameAs links to its LinkedIn and Wikidata pages. Each author page gets Person markup that references the firm by @id. The markup now matches what a recommendation agent would look for when asked to find an English-speaking accountant in that city.

Limitations and things to watch

• Markup must describe what is visibly on the page. Misleading markup can lead to manual actions.
• Valid markup is no guarantee of rich results, a Knowledge Panel or AI citations.
• Consistency matters more than completeness: names and facts in schema should match the page, your profiles and third-party sources exactly.
• Validate with the Schema Markup Validator and Google’s Rich Results Test, and re-check after template changes.

Learn it on MLforSEO

• Structured Data Automation with Python: Detect, Generate and Validate JSON-LD at Scale
• How Structured Data Reinforces Entity Understanding in AI Search
• What Schema.org @id Does and Why Your Entity Graph Falls Apart Without It
• Consistency beats completeness: schema for machines, not results
• AI Search & LLMs: Entity SEO and Knowledge Graph Strategies for Brands (course)