Skip to content
Citable

Definition

JSON-LD

Also known as: JSON for Linking Data, application/ld+json

JSON-LD (JSON for Linking Data) is a W3C standard for expressing linked data as ordinary JSON. On the web it is the format Google recommends for Schema.org structured data: a single script block of type application/ld+json that describes the page's entities — article, author, dates, FAQ, definitions — without touching the visible HTML.

JSON-LD 1.0 became a W3C Recommendation in January 2014; the current version, JSON-LD 1.1, followed in July 2020. The design goal was to let ordinary JSON become linked data by adding a @context that maps plain keys to globally defined terms, so a "name" in one document means the same thing as a "name" in another.

That separation from the HTML is what makes JSON-LD useful for answer engines. A crawler can read one JSON object and learn what the page is (@type), who wrote it (author), when it was published and updated, what it cites, and which selectors hold the direct answer. None of that requires parsing prose or guessing at layout. Three keys carry most of the weight: @context, @type and @id. Giving an entity an @id gives it a stable URL, so the same author or the same glossary term can be referenced consistently from many pages.

json
{
  "@context": "https://schema.org",
  "@type": "TechArticle",
  "headline": "What is Answer Engine Optimization (AEO)?",
  "author": { "@type": "Person", "@id": "https://example.com/about#author" },
  "datePublished": "2026-01-12",
  "dateModified": "2026-08-20"
}

The main risk is drift: markup that says one thing while the visible text says another. Generate the JSON-LD from the same fields that render the page, as described in Structured data for AI answers.

Frequently asked questions

Is JSON-LD better than Microdata or RDFa for answer engines?

For most sites, yes. All three carry the same Schema.org vocabulary, but JSON-LD sits in one script block that can be generated from CMS fields, whereas Microdata and RDFa are spread across HTML attributes and break when templates change. Google recommends JSON-LD for the same reason: it is the easiest to implement and maintain at scale.

Related terms

Guides that use this term

  • Structured data

    Structured data for AI answers: the schema.org types that matter

    Structured data for AI answers is JSON-LD that labels what a page contains: Article or TechArticle for provenance (author, datePublished, dateModified, citation), FAQPage for question–answer pairs, DefinedTerm for definitions, Person with sameAs for authors, BreadcrumbList and WebSite for context, and SpeakableSpecification for the direct answer. Google requires no markup for AI features; its value is removing ambiguity, so generate it from the CMS fields that render the visible text.

    9 min read

  • Technical

    Modelling content for answer engines in a headless CMS!

    A headless CMS content model for answer engines makes every Answer Engine Optimization signal a constrained field, not an editorial habit: a required short answer with a maximum length, key takeaways, FAQ items, sources with URL validation, the author as a relation, and published and updated dates. JSON-LD, Markdown and llms.txt are then generated from those fields, so no representation can disagree with the page.

    9 min read