DefinedTerm · Glossary

What is schema markup

Schema markup is a structured-data vocabulary maintained by schema.org that lets webmasters annotate HTML with machine-readable semantic metadata. Expressed as JSON-LD embedded in the page, it tells Google, Bing and AI answer engines exactly what a page is about, who published it and what claims it makes — without forcing any system to infer meaning from prose alone. Correct implementation unlocks rich results in search and increases the probability of citation in generative AI responses.

edu-lopez-paradaPublicado 27 May 2026Actualizado 27 May 2026

Full definition

Schema.org is a collaborative vocabulary founded in 2011 by Google, Microsoft, Yahoo and Yandex. Its goal was to establish a shared language for describing the meaning of web content so that every search engine could interpret pages consistently. A schema.org type — for example LocalBusiness, FAQPage or HowTo — represents a class of real-world entity. Its properties describe the attributes of that entity in a way any consuming system can parse without ambiguity.

The vocabulary can be expressed in three syntaxes: JSON-LD (the format Google recommends), Microdata and RDFa. JSON-LD sits inside a <script type="application/ld+json"> tag that is entirely separate from the visible HTML. This separation means you can add, edit or remove markup without touching the page layout, and automated validators can check it without rendering the DOM.

When a search engine or AI model processes a page, it reads both the prose and any structured data present. The structured data acts as an authoritative declaration: the page does not just mention a plumber, it declares itself to be the website of a specific plumbing business with a defined address, phone number and service area. That declaration removes the guesswork that prose alone cannot eliminate.

Why it matters in 2026

Generative search — Google AI Overviews, Bing Copilot, Perplexity, ChatGPT search — works by extracting passages from pages and attributing them to a source. Pages that declare their content type, author and factual claims via structured data give these systems a stronger signal than pages that leave all context implicit. According to Google Search Central, well-implemented structured data is eligible for rich results including star ratings, expandable FAQs, step-by-step instructions and knowledge panels — all of which improve organic visibility independently of ranking position.

Format	Recommended by Google	Separate from visible HTML	Maintenance ease
JSON-LD	Yes	Yes	High
Microdata	No (permitted)	No	Low
RDFa	No (permitted)	No	Medium

How to implement

Identify the schema.org type that best describes the page content. Consult the full type catalogue at schema.org to find the most specific applicable type rather than defaulting to the generic WebPage.
Create a <script type="application/ld+json"> block containing the JSON object that corresponds to the chosen type.
Complete the mandatory and recommended properties as described in the Google Search Central documentation for that specific type.
Insert the block in the <head> or anywhere in the <body>. JSON-LD is position-independent and does not affect page rendering.
Validate the output using Google's Rich Results Test at search.google.com/test/rich-results to confirm there are no syntax errors or missing required properties.
Publish the page and monitor rich-result impressions and errors in Google Search Console under the Enhancements section.

Example

{
  "@context": "https://schema.org",
  "@type": "WebPage",
  "name": "Boiler installation services in London",
  "description": "Professional boiler installation covering all London boroughs, with Gas Safe registered engineers.",
  "url": "https://example.co.uk/boiler-installation-london",
  "inLanguage": "en-GB",
  "publisher": {
    "@type": "LocalBusiness",
    "name": "London Boiler Pro",
    "address": {
      "@type": "PostalAddress",
      "addressLocality": "London",
      "addressCountry": "GB"
    }
  }
}

What is schema markup

Full definition

Why it matters in 2026

How to implement

Example

Related terms

Fuentes

Términos relacionados