SchemaFree

Author Schema

Detects Person schema with author credentials like name, jobTitle, url, and sameAs links. Establishes authorship for AI trust signals.

Why It Matters for AI Visibility

Person schema establishes the identity and credentials of your content author in a format that AI engines can parse directly. ChatGPT and Perplexity use author information as a trust signal -- content from identifiable experts with verifiable professional profiles is cited more confidently than content from unknown authors. E-E-A-T (Experience, Expertise, Authority, Trust) is a core quality signal for Google AI Overviews. Person schema with `jobTitle`, `url`, and `sameAs` links provides machine-readable proof of the author's expertise and professional standing. While visible bio text helps human readers, structured Person schema feeds AI engines the exact data points they evaluate for authority scoring. The `sameAs` field is particularly powerful. It connects your author to their broader online presence -- LinkedIn, Twitter, personal website, GitHub. This helps AI engines build an entity graph for the author, linking their content across platforms. An author with a verified LinkedIn profile and active Twitter presence registers as a real, established expert, not a fabricated persona. The more connected the author entity, the stronger the trust signal.

How We Score It

The analyzer searches for Person schema in three locations: standalone `@type: "Person"` blocks in JSON-LD, nested `author` fields within Article or BlogPosting schema, and author arrays containing Person objects. If multiple Person schemas exist, the most complete one is evaluated. Four fields are checked: `name`, `jobTitle`, `url`, and `sameAs`. Each field contributes 2.5 points to your score. All four fields present scores a 10. Three fields scores an 8. Two fields scores a 5. One field scores a 3. No Person schema found at all scores a 0 -- this represents a significant gap in your E-E-A-T signals. A score of 7 or higher (at least 3 fields) passes. Scores of 4 to 6 are partial. Below 4 is a fail.
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How to Improve

  • 1

    Add all four Person schema fields

    At minimum include `name` and `jobTitle`. The `url` field should link to the author's profile page on your site. The `sameAs` array should include LinkedIn and Twitter profile URLs. All four fields earns a perfect 10. Missing any single field costs you 2.5 points.

  • 2

    Nest the Person schema inside your Article schema

    Instead of having a separate Person JSON-LD block, embed it in the Article's `author` field. This directly associates the author with the content, which is the strongest signal for AI engines. The analyzer detects Person schema in both standalone and nested positions.

  • 3

    Add sameAs links to establish cross-platform identity

    Include 2 to 3 social profile URLs in the `sameAs` array: LinkedIn, Twitter/X, personal website, or GitHub. This helps AI engines verify the author exists as a real person with a professional presence. A Person schema with sameAs links registers as a verified entity, not just a name string.

  • 4

    Use jobTitle to signal domain expertise

    "Senior Software Engineer at Google" carries more authority weight than a bare name. The jobTitle field should reflect the author's relevant expertise for the content topic. A security article authored by a "Senior Security Engineer" signals topical authority that AI engines factor into citation decisions.

Before & After

Before
{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "Web Security Best Practices",
  "author": "Jane Smith"
}
After
{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "Web Security Best Practices",
  "author": {
    "@type": "Person",
    "name": "Jane Smith",
    "jobTitle": "Senior Security Engineer",
    "url": "https://example.com/authors/jane-smith",
    "sameAs": [
      "https://linkedin.com/in/janesmith",
      "https://twitter.com/janesmith"
    ]
  }
}

Code Examples

Standalone Person schema with all four fields

{
  "@context": "https://schema.org",
  "@type": "Person",
  "name": "Jane Smith",
  "jobTitle": "Senior Security Engineer",
  "url": "https://example.com/authors/jane-smith",
  "sameAs": [
    "https://linkedin.com/in/janesmith",
    "https://twitter.com/janesmith",
    "https://github.com/janesmith"
  ]
}

Frequently Asked Questions

Should Person schema be standalone or nested inside Article schema?

Both work. The analyzer detects Person schema in standalone blocks, nested in author fields, and in author arrays. Nesting inside Article is slightly preferred because it directly links the author to the specific content. If you already have Article schema, add the Person object there.

What if we have multiple authors?

Use an author array: `"author": [{"@type": "Person", ...}, {"@type": "Person", ...}]`. The analyzer evaluates the most complete Person schema found, so ensure at least one author has all four fields. The others can have fewer fields without hurting your score.

Is the jobTitle field really that important?

Yes. It is one of four scored fields, worth 2.5 points, and it is the primary way to signal domain expertise to AI engines. A security article authored by a "Senior Security Engineer" carries more weight than one by an unidentified writer. Choose a jobTitle that is relevant to the content topic.

Related Factors

Authority

Author Identification

Authority

Author Credentials

Schema

Article / BlogPosting Schema

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