How to Find Entities Manually To Do Semantic SEO

What is Manual Entity Extraction?

Manual entity extraction is the process of identifying semantically relevant entities (people, places, products, services, organizations, tools, events, and abstract ideas) by contextual observation and search engine feature mining — rather than relying on automated NLP tools.

Goal: Build a topical map rooted in first-degree and second-degree entities that improves content’s depth, context, and semantic relevance.

Why It Matters in Semantic SEO

• Entities provide semantic signals that go beyond keywords.
• Helps map intent clusters and contextual relationships.
• Reduces ambiguity and improves machine readability.
• Feeds search engines meaning, not just terms.

Foundational Entity Categories

Category	Examples
Person	Elon Musk, plumber, technician
Place	New York City, bathroom, kitchen
Thing	Tesla, wrench, pipe
Event	Plumbing Emergency, Flooding
Idea	Maintenance, Repair, Hygiene
Organization	Roto-Rooter, Home Depot

Step-by-Step: Manual Entity Mining Techniques

1. Google Image Segmentation

• Search: “Plumber” or “Plumbing Services”
• Inspect: The suggested filters above image results:
  • Tools, Repair, Emergency, Residential, Commercial, Uniform, Sink
• Extract: Semantic variants and closely related concepts.

Avoid irrelevant or decorative terms: “vector,” “logo,” “transparent,” “cartoon.”

2. Wikipedia Hyperlink Mining

Search: Keyword in Wikipedia (e.g., “Plumber”)

Step 1: Extract first-degree entities (directly hyperlinked terms in intro/infobox)

Step 2: Navigate to hyperlinks → extract second-degree entities

Example

• 1st Degree Entities:
  • From “Plumber” Wikipedia Page:
    • tradeperson, sewage, water, drainage, plumbing
• 2nd Degree Entities:
  • From “Drainage” Wikipedia Page:
    • Sub-surface water, waterlogging, agriculture, soils

These hyperlinks represent contextual vectors (will discussed later in broad). Search engines interpret them as entity relations (subject–predicate–object also called triples).

ALSO READ …

• How to extract entities using Google NLP tool
• How to extract entities from Textrazor (Free Tool)
• What is entity recognition in semantic
• What is entity-based content
• What is structured data

3. Entity Expansion via Search Variation

• Change seed keyword: “Emergency plumber,” “Drain cleaning,” “Toilet repair”
• Observe SERP enhancements:
  • Featured snippets
  • “People Also Ask”
  • Related Searches
  • Knowledge Panels

Each variation surfaces unique sets of latent entities.

4. Manual Curation & Filtering

• Compile all found terms into a Google Sheet
• Remove:
  • Duplicates
  • Decorative/UI labels (e.g., “outline,” “transparent”)
  • Brand names (unless contextually relevant)

Term	Entity Type	Keep? (Y/N)
Wrench	Thing	Y
Vector	Design Term	N
Emergency Repair	Event	Y
Mario	Brand	N

Use Case: Building a Topical Map for “Plumber”

Entity	Attribute	Value/Example
Plumber	Services	Drain Cleaning, Toilet Repair
Plumbing Tool	Type	Wrench, Plunger
Plumbing Emergency	Response Time	24 Hours
Pipe Material	Composition	PVC, Copper
Service Area	Location	Brooklyn, NY

Each triple (Entity–Attribute–Value) becomes part of your semantic structure, contributing to contextual clustering in search engines.

Tools That Support This Manual Process

Tool	Purpose
Google Images	Segmented keyword/entity clusters
Wikipedia	Hyperlinked entity discovery
Google Sheets	Deduplication, filtering, tagging
Text Tools	Regex/entity extraction (e.g., TextFixer, TextTools.org)
People Also Ask / Related Searches	Discover user-generated entity queries

Final Thoughts: The Power of Manual Entity Extraction

Manual entity discovery is a semantic layering method. It transforms shallow content into deep knowledge graphs—by connecting concepts, attributes, and contextual queries.

• You are reverse-engineering Google’s understanding.
• You are creating topical authority by clustering related entities.
• You are giving machines the language they understand best: Triples.

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Coming in part 18: How to Extract Entities Using Google NLP Tool

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