Once entities are extracted, scored, and disambiguated via NLP, Google doesn’t just index them—it contextualizes them across:
- The Knowledge Graph
- The Search Engine Results Page (SERP)
- The Entity Database
- And even Retrieval-Augmented Generation (RAG) systems
This is not keyword-based indexing. It is entity-based search understanding, where meaning, relationships, and contextual alignment matter more than match counts.
Step-by-Step: What Happens After Google Extracts Entities?
1. Entity Classification
Google uses structured formats (e.g., schema.org) and NLP outputs to assign each entity a type:
| Term | Classified As |
|---|---|
| Sundar Pichai | Person |
| Organization | |
| iPhone 16 | Product |
| Washington | Ambiguous → Location / Person / Event |
2. Entity Salience & Relevance Scoring
After extraction, entities are ranked by:
- Salience: How important is this entity within the context of the document?
- Sentiment: What is the tone around this entity?
- Contextual Relationships: What other entities is it linked to?
For example, in an article about Sundar Pichai, entities like Alphabet, Google, India, and CEO are tightly clustered and heavily weighted.
3. Knowledge Graph Integration
Once identified and scored, entities are mapped to nodes in Google’s Knowledge Graph, a massive interconnected network with:
- Unique entity IDs
- Predefined types
- Hierarchical relations
- Canonical attributes and values
Examples:
| Entity | Attribute | Value |
|---|---|---|
| Sundar Pichai | Title | CEO of Alphabet |
| Mount Everest | Height | 8,848 meters |
| Tesla | Founder | Elon Musk |
| Washington | Located In | USA |
This creates triples or Entity-Attribute-Value (EAV) data structures that power search panels, featured snippets, and zero-click answers.
ALSO READ …
- How Google detects entities using NLP
- How to help Google find entities on your content
- What is entity-based content
- What is entity attribute value
- What is structured data
4. Search Interface Output (SERP Usage)
Entities extracted from your content appear in:
- Knowledge Panels (People, Places, Organizations)
- Featured Snippets
- People Also Ask
- Rich Results (via Structured Data)
- Shopping and Product Cards
- Image Carousels
- Event Listings
Example: Searching “Sundar Pichai” shows an About panel with connected data (CEO of Alphabet, Indian-American, Born in Madurai, etc.) pulled from multiple sources like Wikipedia, Crunchbase, and your site—if properly marked up.
Why Entity Usage Matters More Than Keywords
1. Entity Disambiguation
Google uses context to disambiguate homonyms:
| Term | Contextual Clue | Interpreted As |
|---|---|---|
| Washington | “White House” nearby | Washington, D.C. |
| Apple | “iPhone 16 Launch” | Apple Inc. |
| Jordan | “NBA Finals” | Michael Jordan (Person) |
This is done using models like:
- Hummingbird (Intent Recognition)
- BERT/MUM (Contextual Embeddings)
- RAG (Retrieval-Augmented Generation)
2. Entity Intent Resolution (via MUM & RAG)
Google uses multi-modal transformers like MUM (Multitask Unified Model) and RAG to:
- Extract facts from multiple sources
- Merge facts into structured graphs
- Generate real-time summaries (e.g., “latest movies by Robert Downey Jr.”)
Connecting the Knowledge Graph
Entities don’t exist in isolation—they are connected. This is called entity relationship modeling.
| Entity A | Relationship | Entity B |
|---|---|---|
| Sundar Pichai | CEO of | Alphabet |
| Alphabet | Owns | |
| Developed | Android | |
| Android | Competes With | iOS |
These connections are stored as graphs and used to:
- Rank websites
- Suggest related queries
- Trigger rich snippets
- Populate Google Discover & Explore tabs
Under the Hood: Retrieval-Augmented Generation (RAG)
The newest phase is RAG, a hybrid of:
- Retrieval: Pull entity facts from Google’s DB (Knowledge Vault, Wikidata, Wikipedia)
- Augmented Generation: Dynamically generate panels, summaries, and contextual results.
For example: Searching “Washington Monument nearest coffee shop” → pulls location data + business listings + context → generates a result set.
Summary: Why This Matters for Semantic SEO
| Concept | SEO Impact |
|---|---|
| Entities in Content | Improve indexing, ranking, and visibility |
| Knowledge Graph Linking | Enables Knowledge Panels, Snippets, and Visual SERPs |
| Structured Data Markup | Helps Google classify entities faster and more accurately |
| Salience & Sentiment | Influence snippet eligibility and query match precision |
| RAG & MUM | Power conversational and voice search |
Actionable Recommendations
- Use E-A-V Format in content (Entity → Attribute → Value)
- Map Entities to Wikipedia or Wikidata
- Implement JSON-LD Schema Markup
- Use NLP Tools (Google NLP API, TextRazor, InLinks)
- Avoid Entity Ambiguity – Use surrounding context (e.g., “Washington, D.C., the U.S. capital”)
- Monitor Knowledge Graph Visibility – Use tools like Kalicube, WordLift
Closing Insight
“Google is not a search engine. It’s a knowledge engine.”
To align with Google’s evolution, your content must move from keyword matching to entity modeling.
So, you’re no longer writing for users alone—you’re also educating machines, one semantic structure at a time.
In the next part 22: How to Help Google Find Entities on Your Content Page
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.