Structured Data
Structured data
Markup (typically JSON-LD using Schema.org vocabulary) that tells search engines and LLMs what the entities and relationships on a page are. Increasingly important as both Google and generative systems converge on entity-level understanding.
What it is
Structured data is machine-readable markup, typically JSON-LD using Schema.org vocabulary, that states what entities a page describes and how they relate. It turns implicit meaning in prose into explicit, parsable assertions.
Why it matters
As Google and generative systems converge on entity-level understanding, structured data is a primary signal for disambiguating entities and for being cited accurately in AI-generated answers.
How it works
Identify the page's core entities and relationships, map them to appropriate types and properties, and emit them as JSON-LD that mirrors the visible content.
When it applies
It applies to any page whose meaning, entities, or relationships you want machines to interpret without inferring them from text alone.
Examples
- A recipe page exposing ingredients, steps, and cook time as Recipe properties
- An author page connecting a Person to their published Articles and sameAs profiles
- An events listing emitting Event nodes with dates, locations, and organisers
How it is measured
- Percentage of key entities on a page covered by markup
- Agreement between structured claims and rendered content
- Number of entities successfully resolved to known knowledge-graph nodes
- Validation pass rate across the site's templates
Related terms in Structured Data
- JSON-LDThe recommended syntax for embedding Schema.org structured data on a page. Lightweight, decoupled from page HTML, and increasingly the format LLMs prefer when retrieving structured facts.
- Knowledge graph entityA node in Google's Knowledge Graph representing a real-world thing (person, place, organisation, work). Strong entity signals are prerequisite for Search Profile eligibility and consistent AI Overview attribution.
- Retrieval-augmented generationA generation approach where an LLM pulls relevant documents at query time and uses them as the source for its answer. The pattern behind most enterprise AI search products and Perplexity-style answer engines.
- Schema.orgThe shared vocabulary for structured-data markup used by Google, Microsoft, and major search engines. As of June 2026, Schema.org publishes monthly aggregate adoption statistics by type.