The knowsAbout Property

Of all the schema properties available for entity recognition, knowsAbout is arguably the most directly relevant and the most underused. It does exactly what the name suggests: it tells search engines what topics your entity has expertise in.

While Google has not confirmed that knowsAbout directly influences rankings, it feeds into entity understanding. And as AI search systems grow, explicit expertise declarations become increasingly important. The system needs to know what you know. This property tells it.

What knowsAbout Does

The knowsAbout property accepts a text string, a URL, or a Thing object. It can be applied to both Person and Organization types. When you declare knowsAbout, you are creating a direct, machine-readable link between your entity and a topic.

The Specificity Spectrum

The value of knowsAbout depends entirely on specificity. A vague declaration is noise. A precise declaration is a signal. Here is how specificity affects the quality of the entity association.

The goal is not to pick the most obscure term you can find. The goal is to match the language your industry uses to describe your actual expertise. If "entity SEO" is a recognized term in your field, use it. If "structured data for healthcare compliance" describes what you do, that is better than "SEO."

Building Your knowsAbout List

Your knowsAbout list should contain 15 to 20 specific topics, ordered from most central to least central. Think of it as concentric rings: your core expertise at the center, related competencies in the middle, and supporting knowledge at the edges.

Every topic in your knowsAbout list should meet two criteria. First, you can genuinely demonstrate expertise in it through published content, client work, or professional experience. Second, it aligns with the entity associations you are trying to build. Adding random topics you happen to know about dilutes your topical signal.

Implementation Approaches

There are three ways to implement knowsAbout, each with increasing semantic strength.

Plain text (weakest):

"knowsAbout": ["entity SEO", "knowledge graphs", "structured data"]

URL references (stronger):

"knowsAbout": [
  "https://en.wikipedia.org/wiki/Knowledge_graph",
  "https://en.wikipedia.org/wiki/Search_engine_optimization",
  "https://schema.org/"
]

DefinedTerm objects (strongest):

"knowsAbout": [
  {
    "@type": "DefinedTerm",
    "name": "Entity SEO",
    "description": "SEO strategy centered on entity identity and knowledge graph optimization",
    "sameAs": "https://en.wikipedia.org/wiki/Search_engine_optimization"
  }
]

The DefinedTerm approach gives the system the most context. It provides a name, a description, and a link to an authoritative definition. Not every topic needs this level of detail, but your core 3 to 5 topics should use it.

Aligning knowsAbout with Your Content

Your knowsAbout declarations should align with your actual content architecture. If you claim to know about "entity SEO" but have no content hub on the topic, the structured data contradicts your site structure. Search engines notice this kind of inconsistency.

Cross-reference your knowsAbout list with your topical clusters from Module 2. Every core topic in your knowsAbout should have corresponding content depth on your site. Every content hub on your site should be reflected in your knowsAbout. The alignment between declared expertise and demonstrated expertise is what builds recognition.

Further Reading

• knowsAbout Property Documentation (Schema.org)
• What is knowsAbout Schema and How Can It Improve SEO? (Dental Design Marketing)
• Using Schema.org Markup to Demonstrate Your E-A-T (Optiminder)
• When Should I Use sameAs Versus knowsAbout? (Will Scott)