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What is schema markup? The complete guide for WordPress

By Anis Harbaoui, SEO specialist

Schema markup is structured data, written with the Schema.org vocabulary, that tells search engines what a page is about: which entity it describes, who published it and how it connects to other entities. On WordPress it is output as JSON-LD by an SEO plugin. This guide covers how it works, which types still matter in 2026, how to build it as one connected graph, and how to measure the result.

What is schema markup?

Schema markup is a machine-readable description of a page built from the types and properties defined at Schema.org, a vocabulary founded in 2011 by Google, Microsoft, Yahoo and Yandex. HTML tells a browser how to display text; schema markup tells a search engine what that text means: that “NexalSEO” is a SoftwareApplication, that it is published by an Organization, and that this page is a guide written by a Person.

A minimal example

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Organization",
  "name": "Example",
  "url": "https://example.com/",
  "logo": "https://example.com/logo.png"
}
</script>

Schema.org vs JSON-LD vs microdata

Which rich results still exist in 2026?

Before adding a type for its visual result, check that the result still exists. Google restricted or retired several features recently:

Structured data should be a graph

The difference between basic and advanced markup is the graph. Instead of separate blocks for Organization, Article and Breadcrumb, a connected graph gives each entity a stable @id and references it wherever it appears. Google reads one coherent set of facts instead of fragments that may contradict each other.

{
  "@context": "https://schema.org",
  "@graph": [
    { "@type": "Organization", "@id": "https://example.com/#organization",
      "name": "Example",
      "sameAs": ["https://www.linkedin.com/company/example",
                 "https://www.wikidata.org/wiki/Q_ID"] },
    { "@type": "WebSite", "@id": "https://example.com/#website",
      "publisher": { "@id": "https://example.com/#organization" } },
    { "@type": "WebPage", "@id": "https://example.com/guide/#webpage",
      "isPartOf": { "@id": "https://example.com/#website" } },
    { "@type": "Article", "@id": "https://example.com/guide/#article",
      "mainEntityOfPage": { "@id": "https://example.com/guide/#webpage" },
      "publisher": { "@id": "https://example.com/#organization" },
      "about": { "@type": "Thing", "name": "Schema.org",
                 "sameAs": "https://www.wikidata.org/wiki/Q3475322" } }
  ]
}

Three rules make it work: an @id never changes between pages; nodes reference each other by @id instead of repeating data; sameAs only points to profiles that describe the same entity. This is exactly how NexalSEO’s schema module builds every page.

Quality rules Google enforces

1. Match visible content. Prices, ratings, dates and authors in markup must appear on the page.
2. No hidden or invented reviews. Self-serving reviews of your own business are not eligible for review stars.
3. Mark up the main content. Do not describe products or events that are not the subject of the page.
4. Keep it current. Out-of-stock products marked as in stock can cost merchant features.
5. Violations can lead to a manual action that removes rich results site-wide.

Does schema markup help with AI search?

What is established: AI Overviews are built on Google’s index and systems, ChatGPT Search and Copilot rely heavily on Bing’s index, and both search engines use structured data to understand pages. Clean, unambiguous entities therefore help the systems that AI answers depend on. What is not established: there is no public evidence that adding a given schema type directly increases AI citations. Treat structured data as a clarity layer, alongside clear answers in the visible text. Our AI Overviews guide covers the rest.

Further reading: Getting started with Schema.org.

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