How Google Use Entities after Extraction

How to Help Google Find Entities on Your Content Page – Part 22

by

“If Google can detect entities, why do we need to help it?”

Because machine understanding isn’t perfect—especially with ambiguous content, missing markup, or disorganized information architecture.

Objective of This Phase

The core mission here is to explicitly pass entity information to Google in a machine-readable format.

  • This format is schema.org Structured Data, ideally in JSON-LD.
  • Why? Because it allows you to define:
    • What the entity is (e.g., @type: Product, Person, Organization)
    • What its attributes are (e.g., name, brand, location)
    • How it relates to other things (sameAs, offers, relatedLink)

<script>

“about”: [

{“@type”: “Thing”, “name”: “Google_Knowledge_Graph”, “sameAs”: “https://en.wikipedia.org/wiki/Google_Knowledge_Graph”},

{“@type”: “Organization”, “name”: “Google”, “sameAs”: “https://en.wikipedia.org/wiki/Google”},

{“@type”: “Thing”, “name”: “knowledge graph”, “sameAs”: “https://en.wikipedia.org/wiki/Knowledge_Graph”},

],

“mentions”: [

{“@type”: “Thing”, “name”: “concept”, “sameAs”: “https://en.wikipedia.org/wiki/Concept”},

{“@type”: “Thing”, “name”: “users”, “sameAs”: “https://en.wikipedia.org/wiki/User_(computing)”},

{“@type”: “Thing”, “name”: “entities”, “sameAs”: “https://en.wikipedia.org/wiki/Named_entity”},

{“@type”: “Thing”, “name”: “data”, “sameAs”: “https://en.wikipedia.org/wiki/Data”},

{“@type”: “Thing”, “name”: “INPUT”, “sameAs”: “https://en.wikipedia.org/wiki/Information”},

{“@type”: “Thing”, “name”: “result”, “sameAs”: “https://en.wikipedia.org/wiki/Result”}

]

}

</script>

This schema is doing 3 things:

1. Categorizing the concept into either thing, person, place or organization

  • “@type”: “Thing”; “@type”: “Organization”

2. Expressing the Keyword you are using

  • “name”: “Google_Knowledge_Graph”,

3. Connecting that keyword to something that has a KGMID, such as a Wikipedia article

“sameAs”: https://en.wikipedia.org/wiki/Google_Knowledge_Graph

What Kind of Schema Should You Use?

Let’s break it down by page type:

1. Homepage

  • Organization schema if you’re a company
  • LocalBusiness schema if you have a local office or store
  • WebSite or WebPage for general presence

2. Category Pages

  • Often don’t require rich schemas
  • Optionally, use ItemList or breadcrumb schemas

3. Product Pages

  • Use Product schema
  • Include attributes: name, image, price, availability, aggregateRating

4. Service Pages (Local SEO)

  • Use Service + LocalBusiness
  • Include: areaServed, audience, provider, sameAs, description, hasOfferCatalog

5. Content Pages (Blog, Articles)

  • Use Article or BlogPosting
  • Define: headline, author, datePublished, publisher, sameAs

Core Entity Markup Pattern (E-A-V)

Using E-A-V (Entity-Attribute-Value) in schema:

{
  "@context": "https://schema.org",
  "@type": "Product",
  "name": "iPhone 16",
  "brand": {
    "@type": "Organization",
    "name": "Apple Inc.",
    "sameAs": "https://en.wikipedia.org/wiki/Apple_Inc."
  },
  "offers": {
    "@type": "Offer",
    "price": "1299.00",
    "priceCurrency": "USD",
    "availability": "https://schema.org/InStock"
  }
}

This markup anchors the entity and its attributes into Google’s NLP pipeline. If you omit this, Google may infer, but never with full confidence.

Use of sameAs

  • Links entities to canonical sources (Wikipedia, LinkedIn, company websites)
  • Disambiguates meaning (e.g., “Plumbing” as a service, not a song or brand)
  • Examples:
"sameAs": [
  "https://en.wikipedia.org/wiki/Plumbing",
  "https://www.facebook.com/mrrooter"
]

Testing Your Schema

Use tools like:

Make sure:

  • Entities are present and typed
  • Attributes are accurate and complete
  • No warnings (e.g., missing required fields like name, url, image)

Common Mistakes

  • Using incorrect schema types (Article instead of Service)
  • Missing sameAs URLs (no entity disambiguation)
  • Adding markup without real on-page content to match
  • Keyword stuffing inside structured data

Best Practices

PracticeBenefit
Use sameAs with authoritative sourcesBoosts Google’s confidence in entity mapping
Add schema to key pages onlyImproves efficiency and relevance
Validate all JSON-LD regularlyPrevents crawl/indexing errors
Align schema content with visible contentAvoids spam/penalties
Include reviews, ratings, and availabilityEnables rich results (stars, stock, etc.)

Semantic SEO Insight

Structured Data is not just for SERP features—it’s for machine understanding.

It tells Google:

  • What this page is about
  • Which entities it connects to
  • Why this page deserves ranking over others

Final Note on Entity-Based Indexing

Google uses this structured data to:

  • Enrich the Knowledge Graph
  • Disambiguate entity mentions
  • Improve personalization
  • Power voice search, Discover, and Google Lens

In essence, you are feeding the machine—not just decorating the SERP.

Next in Part 23: What is Query Semantics? Understanding Context in Search Queries

Disclaimer: This [embedded] video is recorded in Bengali Language. You can watch with auto-generated English Subtitle (CC) by YouTube. It may have some errors in words and spelling. We are not accountable for it.

Leave a Comment