Entity Discovery in SEO: Map Concepts Google Trust

Introduction: Why I built this Entity Discovery Guide (and who it’s for)

I still remember the frustration of publishing a “keyword-perfect” article that flatlined. It had the right density, the right length, and impeccable grammar, yet it was invisible. Meanwhile, a competitor’s page with half the word count was ranking #1 and getting cited in Google’s AI Overviews. The difference wasn’t the keywords; it was the entity discovery in SEO strategy behind it.

Search has fundamentally shifted. With estimates suggesting that 60%–70% of searches could be zero-click by late 2025 , and AI Overviews appearing in roughly 13% of queries (and growing), optimizing for strings of text isn’t enough. Machines need to understand the concepts (entities) on your page to trust them.

I wrote this guide for the intermediate SEO operator who needs a repeatable system, not high-level theory. Here is the exact workflow I use to identify, map, and implement entities to future-proof content visibility.

Quick answer: What is entity discovery in SEO?

If you are in a rush, here is the bottom line: Entity discovery in SEO is the process of identifying the distinct, real-world concepts—people, organizations, places, products, or ideas—that are relevant to your topic and ensuring search engines can unambiguously recognize them.

Think of it this way: If keywords are the words you speak, entities are the ID cards you show to prove you are who you say you are. When I optimize for entities, I am effectively handing Google a map of relationships rather than a bag of words. This usually involves:

• Unique Identification: Distinguishing “Apple” (the fruit) from “Apple” (the tech giant).
• Disambiguation: Using structured data and context to clear up confusion.
• Connection: Linking your primary topic to other authoritative concepts in a Knowledge Graph.

I use this approach specifically when launching new sites, doing a rebranding, or expanding into a topic where trust signals (E-E-A-T) are critical.

Why entities beat keywords now (especially with AI Overviews)

Here is the thing: keywords are still necessary for understanding user demand, but entities are how machines understand meaning. In the past, Google matched query words to page words. Today, LLMs (Large Language Models) and search algorithms look for a web of relationships.

When I analyze why a page gets picked up by an AI Overview, it is rarely because it repeated a keyword 15 times. It is because the search engine recognized the page as an authoritative source for a specific entity relationship. With zero-click searches projected to dominate, being the “cited source” in an AI summary is the new ranking #1. If your content is just a collection of keywords without clear entity mapping, you are practically invisible to these new systems.

Table: Keywords vs entities (what changes in how I optimize)

Feature	Keyword Optimization	Entity Optimization
Focus	Matching search strings	Defining concepts & relationships
Ambiguity	High (depends on phrasing)	Low (disambiguated via ID/Schema)
Primary Goal	Rank for a specific query	Build topical authority & trust
AI Impact	Low (often summarized/skipped)	High (source of facts for AI)
Measurement	Rankings, Traffic	Visibility, Citations, Share of Voice

The takeaway: Keywords get you found; entities get you understood and cited.

How entity discovery in SEO actually works (in plain English)

Let’s strip away the jargon. Search engines build a massive mental map of the world called a Knowledge Graph. When you publish content, you are essentially asking to add a dot to that map and draw lines connecting it to other dots.

For example, let’s say I am optimizing a page for a SaaS company selling “Project Management Software.”

• The Entity: The software itself (Product).
• The Relationships: It helps “Agile Teams” (Organization Type), it integrates with “Slack” (Product), and it solves “Resource Allocation” (Concept).

If my content just says “best project management software” repeatedly, Google makes a guess. But if I explicitly map these relationships using clear nouns, internal links, and schema, I am confirming to the search engine: “Yes, this product is related to Agile, not Waterfall construction methodologies.” This clarity creates the confidence required for AI systems to use my content as an answer.

Entities, attributes, and relationships: the three things I look for

When I scan a topic, I look for three specific elements:

• Entities (The Nouns): The core subjects. Example: Tesla Model 3.
• Attributes (The Adjectives/Properties): Facts belonging to the entity. Example: Range, Price, Battery Type, 0-60 Time.
• Relationships (The Verbs/Connections): How it connects to others. Example: Tesla Model 3 (is manufactured by) Tesla Inc.; (competes with) BMW i4.

What AI Overviews likely need from my page to include it

While no one can guarantee inclusion in AI Overviews, I have noticed a pattern in the pages that win. They tend to provide grounded facts. The AI seems to prefer content where the entities are distinct and the claims are corroborated by other authoritative entities on the site. If I make a claim, I support it with data or a link to a known entity. Clarity reduces the hallucination risk for the AI, making your page a safer bet for a citation.

The step-by-step workflow I use for entity discovery in SEO

I don’t rely on gut feeling for this. I use a specific workflow to ensure every piece of content is entity-rich without sounding robotic. Here is the process I follow for every major asset.

Step 1–2: Lock the intent + list candidate entities (fast, without overthinking)

First, I define the search intent (e.g., “How to choose a CRM”). Then, I start extracting candidate entities. I don’t use expensive tools immediately; I start with what is free and available.

• Google’s “People Also Ask”: These are direct clues about related concepts users care about.
• Wikipedia & Knowledge Panels: I look up the main topic. The bolded links in the first paragraph of Wikipedia are usually the most critical related entities.
• Competitor Headings: I scan the top 3 results. What nouns are they using in their H2s and H3s?

Sanity Check: I usually end up with a list of 20–30 potential entities. That is too many, but it is a good starting point.

Step 3–4: Prioritize entities and map relationships (my simple scoring method)

I can’t include everything, or the article will read like an encyclopedia. I prioritize using a simple 0–3 score:

• 3 (Must Have): Essential to the definition (e.g., “Contact Management” for a CRM article).
• 2 (Should Have): Adds depth or answers a common FAQ (e.g., “Salesforce” as a competitor).
• 1 (Nice to Have): Tangential context (e.g., “History of CRMs”).
• 0 (Cut): Irrelevant noise.

Once scored, I map the relationships. I ask: “Does Entity A cause Entity B? Is Entity C a type of Entity A?” This mental map becomes the structure of my article.

Step 5: Turn entity research into a content brief (and scale it with Kalema)

This is where the rubber meets the road. I take my prioritized list and slot them into an outline. Top-tier entities become H2s. Attributes become bullet points. Related entities become internal link targets. When I’m managing a high volume of content, manual briefing can get slow. I often use an SEO content generator to help structure these entity-rich outlines efficiently. However, I always ensure the output is fact-checked. Even the best AI content writer needs a strategist to define the entity list explicitly to ensure the final draft captures the nuance required for ranking.

Step 6–8: On-page implementation (headings, internal links, schema)

Finally, I implement the entities on the page. Here is my checklist:

• Headings: The primary entity must be in the H1. Secondary entities should appear in H2s naturally.
• Introduction: I define the primary entity clearly in the first 100 words.
• Internal Links: I link the first mention of a related entity to its respective hub page on my site.
• Images: I use file names and alt text that describe the entity, not just the keyword.

Build entity assets that search engines can trust: entity hubs + schema (JSON-LD)

One of the biggest wins I have found in entity discovery is the creation of “Entity Hubs.” These are canonical pages that define a specific concept for your website. Instead of explaining what “Cloud Computing” is on 50 different blog posts, I create one master definition page (the Hub) and link back to it.

How I structure an entity hub page (template)

You can copy this structure for your own site. It works for services, products, or core industry concepts.

• H1: [Entity Name]
• Definition Block: A clear, encyclopedia-style definition.
• Attributes Table: Key specs or facts.
• Relationships: “Related to,” “Part of,” or “Alternative to.”
• Proof/Citations: Links to external authority sources (like regulations or patents).
• Internal Links: “Articles about [Entity Name].”
• FAQ Schema: Answering the basic questions (Who, What, Where).

Schema implementation notes I don’t skip

Schema Markup (JSON-LD) is how we translate our content into the machine’s native language. I stick to the standards:

Entity Type	Schema Type	Critical Properties
Company	Organization / LocalBusiness	name, logo, sameAs (socials), contactPoint
Blog Post	Article / BlogPosting	headline, author, about (mentions primary entity)
Product	Product	name, description, brand, offers (price)

A warning for beginners: Do not mark up content that isn’t visible on the page. That is a quick way to get a manual penalty. I always validate my code using the Rich Results Test before publishing.

Measure success: entity-based SEO KPIs (and what to track for GEO)

Measuring entity SEO is tricky because Google Search Console (GSC) is still keyword-centric. However, I look for proxies that tell me my entities are being understood.

Table: KPI definitions and how I’d track them as a beginner

KPI	What it indicates	How I track it
Entity Query Growth	Users associate your brand with the topic	GSC: Filter for Queries containing [Brand] + [Topic]
AI Overview Appearance	Machine trust and citability	Manual checks or SERP tracking tools (checking top 20 queries)
Citation Mentions	Authority in the ecosystem	Backlink tools (looking for brand mentions on topical sites)
Hub Page Traffic	Internal navigation success	GA4: Landing page visits to Entity Hubs

I track these monthly. In my experience, AI visibility is noisier than standard rankings, so I look for 3-month trends rather than daily fluctuations.

Tools & automation: turning entity research into publishable content at scale

Process is everything. When I’m producing one article, I can do this manually. When I’m producing fifty, I need automation that doesn’t sacrifice quality. I use tools to handle the repetitive data extraction and drafting, while I focus on the strategy.

For the drafting phase, I often leverage an AI article generator to handle the heavy lifting of sentence construction based on my entity maps. This ensures that the relationships I mapped out are actually written into the text. Then, for publishing, an Automated blog generator helps streamline the formatting, schema insertion, and metadata setup, pushing directly to WordPress.

My ‘quality gates’ before anything goes live

Automation is powerful, but it requires supervision. I never publish without passing these gates:

1. Entity Coverage Check: Did we include the top 3 prioritized entities?
2. Clarity Check: Is the primary definition easy to read? (I read the intro out loud).
3. Schema Validation: Does the JSON-LD pass the validator without errors?
4. Citation Check: Are all claims supported by internal or external links?
5. Tone Check: Does it sound like a human expert, or generic filler?

Common mistakes in entity discovery (and how I fix them)

I have made plenty of mistakes in this area. Here are the most common ones so you can avoid them.

• Mistake: Confusing keywords with entities.
  Fix: Remember, “running shoes” is a keyword; “Nike Pegasus 40” is an entity. Optimize for the specific thing, not just the category string.
• Mistake: Overstuffing the entity list.
  Fix: I stick to 1 primary entity and maximum 3-5 secondary entities per page. Anything more dilutes the signal.
• Mistake: Inconsistent Naming (The “Apple” problem).
  Fix: I use the exact same name for the entity across my Hub page, Schema, and GMB profile. Consistency builds confidence.
• Mistake: Ignoring Internal Links.
  Fix: If I mention a core entity, I must link to its Hub page. This creates a physical connection in the site structure that mimics the knowledge graph.
• Mistake: Schema that doesn’t match text.
  Fix: If I mark up a “FAQPage” in schema, those questions and answers must be visible text on the page. Discrepancies lead to distrust.

FAQs about entity discovery in SEO

What is ‘entity discovery’ in SEO?

Entity discovery is the practice of structuring content so search engines can identify specific people, places, or concepts (entities) and their relationships. Unlike keyword matching, it focuses on meaning and context.

Why are entities more valuable than keywords now?

Keywords help you capture search demand, but entities help machines understand what your content actually means. With the rise of AI, search engines rely on these entity relationships to verify facts and generate accurate answers.

What are AI Overviews and why do they matter?

AI Overviews are the AI-generated summaries at the top of search results. They matter because they push traditional links down; if your entities aren’t clear enough to be cited in these summaries, you lose visibility.

How do I start optimizing for entity discovery?

Start small: Audit your content to find your core topics. Create one “Hub” page for your most important entity. Add Organization or Product schema to it. Then, ensure all other pages linking to it use consistent anchor text.

How is success measured in entity-based SEO?

I measure success through a mix of traditional traffic metrics and new indicators like appearance in AI Overviews, growth in brand+topic search queries, and the number of rich snippets (like Knowledge Panels) my site earns.

Conclusion: My 3-part recap + next actions to take this week

We have covered a lot, but don’t let the technical side paralyze you. Here is the recap:

• Shift your mindset: Stop writing for strings of text; start defining concepts and relationships.
• Build the assets: Create Entity Hubs and use Schema to label your content clearly.
• Trust the process: Visibility in the AI era comes from being the most corroborated, clear source of truth.

Your next actions for this week:

1. Pick one high-value page on your site.
2. Identify the primary entity and 3 related entities using the “People Also Ask” method.
3. Update the content to define these entities clearly and add internal links to related pages.
4. Add basic JSON-LD schema to that page.

If you take these steps, you are already ahead of 90% of the competition still stuck on keyword density.