Person schema for authors: structured data in 2026

Person is a structured data type that describes an author in machine-readable form — name, role, profiles, and areas of expertise — and helps Google and AI understand who created the content. Two things do most of the work: the sameAs property, with links to profiles like LinkedIn or Wikidata, and a stable @id that, repeated across every article, improves the odds the author resolves into a single entity. This guide walks through how to implement Person data, code example included.

What Person solves

When search engines and some AI systems interpret content, they work with entities and the relationships between them, not just keywords. Who writes the content carries growing weight — in other words, author credibility (E-E-A-T). Person data describes exactly that on the technical side: who the author is, what they know, and where else on the web that can be verified.

This article is the technical counterpart to how you build an author entity for AI in the first place — that one covers the content and the strategy, this one covers the specific structured data.

The key Person properties

You don’t have to fill in everything, but some properties carry more weight than others.

sameAs and @id — the heart of the author entity

Two properties tend to matter most in practice. sameAs gives AI and Google the links that help identify the author reliably — as a general rule, credible, well-maintained profiles (say LinkedIn and Wikidata) make it easier to tell the author apart from people with the same name. @id is the stable identifier you repeat on every one of the author’s articles, which improves the odds that systems tie the individual mentions to a single author.

A JSON-LD example

Here’s what a simplified Person definition for an author might look like on their profile page:

{
  "@context": "https://schema.org",
  "@type": "Person",
  "@id": "https://example.com/#author-sarah-miller",
  "name": "Sarah Miller",
  "url": "https://example.com/about/sarah-miller/",
  "jobTitle": "AI SEO consultant",
  "worksFor": { "@type": "Organization", "name": "Your Company" },
  "knowsAbout": ["AI SEO", "structured data", "e-commerce"],
  "sameAs": [
    "https://www.linkedin.com/in/sarah-miller/",
    "https://www.wikidata.org/wiki/Q000000"
  ]
}

The author’s own profile on the same site belongs primarily in url; sameAs points mainly to other public profiles and reference identities (LinkedIn, Wikidata). In an article you then reference the author through that same @id, so you define them once instead of repeating the whole block on every page. In the article’s data (Article), you point to the author through author and to the publisher through publisher:

{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "Article title",
  "author": { "@id": "https://example.com/#author-sarah-miller" },
  "publisher": { "@type": "Organization", "name": "Your Company" }
}

This is a simplified example. In practice it’s usually worth defining the publisher (Organization) separately and consistently across the site — on larger sites, with its own @id as well.

How to implement Person data

1. 1

   Define the author on their profile page

   Add JSON-LD with the Person type to the author page — name, role, description, and a link to the profile. The name in the data has to match exactly how the author is credited on the articles.

2. 2

   Add links to their profiles

   In sameAs, list the URLs that help identify the author reliably — LinkedIn, Wikidata, and any other public profiles that genuinely represent them. Credible, well-maintained profiles make that identification easier.

3. 3

   Assign a stable identifier and repeat it

   Give the author one stable @id and use that same one on every one of their articles, which improves the odds that systems tie the individual mentions to one author rather than to many different people.

4. 4

   Connect the article to the author

   In the article's data, point to the author through their @id and to the publisher through publisher. That way the author is defined once and articles simply reference them.

5. 5

   Confirm it matches the visible content

   Check that the data matches what's actually visible on the page. Don't invent credentials or profiles the author doesn't have; a mismatch is a risk.

Common mistakes

What to take away

• Person data describes the author as an entity — name, role, profiles, and expertise, for AI and Google alike.
• sameAs is among the most important properties — links to LinkedIn and Wikidata help separate the author from people with the same name.
• Repeat one stable @id across every article so the author resolves into a single entity.
• The article references the author through @id — you define the author once.
• The data has to match the visible content — no invented credentials.
• Person data helps make clear who the author is — verify the result with an AI visibility test.

For transparency: the description of the Person properties draws on public Schema.org documentation and analyses available as of July 2026. The statistics cited (higher citation rates, for example) are directional findings from specific GEO studies, not a universal effect; the actual impact on visibility varies by site and topic and shifts over time. How the content on this site is produced is described on the author page.