Entity-based SEO: An explainer for SEOs and content marketers

Stronger entity signals give a source more chances to be cited, referenced, and ranked. As Google and AI engines move toward mapping how concepts relate to one another and evaluating whether content meaningfully contributes to a subject’s broader ecosystem, entity-based SEO becomes more important.

This guide covers what entities are in SEO, how they differ from keywords, where to find the ones that matter, how to structure content around entity relationships, and how to measure whether the strategy is working.

Table of Contents

• What are entities in SEO?
• Why Entity-Based SEO Matters for Content and SEO Marketers
• How to Find Entities for SEO
• How to Plan Topic Clusters With SEO Entities
• How to Measure and Report on Entity-Based SEO Strategy
• Frequently Asked Questions About Entity-Based SEO

What are entities in SEO?

Entities include people, organizations, places, products, and concepts that search engines identify and connect within the Knowledge Graph.

The Knowledge Graph stores relationships between entities, and these relationships help systems interpret meaning instead of relying on exact-match phrases — so when content makes those connections clear, visibility improves across multiple related queries, not just one primary term.

Concrete examples:

• HubSpot is an organizational entity linked to CRM software, marketing automation, and content strategy.
• Email marketing connects to newsletters, automation platforms, and lead nurturing.

These relationships function as semantic signals that help Google understand how topics fit together — and whether a page meaningfully contributes to a subject's broader ecosystem.

That system-level understanding makes entity-based SEO essential for visibility in both traditional and AI-powered search. This is especially important for enterprise SEO, where larger brands need a structured entity presence.

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Examples of Entities in Search Results

Entity SEO focuses on identifiable entities and their relationships. These concepts aren't abstract, and Google uses entities in Knowledge Panels and other SERP features every day. Here's where to spot them.

Knowledge Panels

Knowledge panels appear on the right side of search results for well-established entities such as brands, public figures, and organizations.

Search "HubSpot," and Google surfaces a panel that includes the company's description, founding date, headquarters, and related entities — all pulled directly from the Knowledge Graph. That panel exists because Google has enough entity signals to confidently describe what HubSpot is and how it connects to other concepts.

Google Business Profiles

Google Business Profiles work the same way at a local level. When someone searches for a restaurant or service business, the profile that appears — with hours, reviews, and location — is Google's entity record for that business. The more complete and consistent that information is across the web, the stronger the entity signal.

Image and Product Results

When Google surfaces a carousel of product images for “noise-canceling headphones,” it’s not matching text — it’s identifying product entities and grouping them by attribute. The same logic applies to recipe carousels, book results, and event listings.

People Also Ask Panels

People Also Ask panels reveal entity relationships directly. Each question in the box represents a concept Google considers semantically connected to the original query. These aren’t guesses — they’re a map of how the Knowledge Graph links ideas together.

Related Searches

Related searches at the bottom of a results page show which entities are most closely related to the original query. They’re one of the fastest ways to identify which concepts belong in a content cluster.

Entity Disambiguation

When Google encounters a word or phrase that could refer to multiple things, it uses surrounding context to determine meaning, a process called entity disambiguation. Entity disambiguation clarifies which specific entity a page refers to.

“Apple” is the classic example. Type it into Google without context, and results lean toward Apple Inc. — the tech company — because that entity carries more search weight. Add the word “recipe” or “orchard,” and the results shift entirely toward the fruit.

Same word, different entity. The surrounding context changed the interpretation.

“Mercury” works the same way. In isolation, Google has to decide: the planet, the Roman god, the element, the car brand, or Freddie Mercury? Context, other entities on the page, surrounding queries, and the site’s topical history help determine which interpretation wins.

This matters for SEO and answer engine optimization (AEO) because content that fails to signal which entity it’s about risks being misread clearly. Clear, consistent entity signals, reinforced through schema, internal linking, and explicit naming conventions, reduce that ambiguity.

How are entities different from keywords?

Entities represent concepts and carry context, relationships, and attributes. Keyword SEO focuses on target search phrases. That distinction determines whether search engines understand meaning or just match text.

For content marketing, focusing on entities helps Google and AI-powered search engines understand how a brand fits into broader topics, not just which terms to rank for.

Google’s Knowledge Graph links brands, tools, topics, and attributes through entity connections in ways that keywords alone cannot capture. This is why pages often rank for multiple related queries even without exact keyword matches. A page optimized for “email automation” may also rank for “AI marketing workflows” when both concepts share strong semantic ties.

Carolyn Shelby, principal SEO at Yoast, offers another perspective. “Keyword SEO is basically working on a flat map, while entity SEO lives in three-dimensional space,” she explains. “In the retrieval layer, LLMs treat concepts, brands, authors, and facts like stars clustered in constellations determined by topic and relevance.”

The entities that get pulled into AI-generated answers are the ones with enough gravity — the well-established, strongly connected concepts that LLMs recognize as authoritative. Shelby notes, “Keywords just help you appear on the map. Entities determine whether you ‘shine brightly’ enough to be selected.”

Keyword SEO vs. Entity SEO: At a Glance

Why Entity-Based SEO Matters for Content and SEO Marketers

Entity-based SEO strengthens topical depth, improves relevance across clusters, and helps search engines interpret how content fits within broader subject areas. Instead of relying on isolated keywords, entity relationships show how concepts connect — a signal that matters for both SERPs and AI-generated answers.

According to research from Fractl and Search Engine Land, 66% of consumers believe AI will replace traditional search within five years, and 82% find AI search more helpful than traditional SERPs. As Kelsey Libert, co-founder at Fractl, notes, “This highlights the need for marketers to prioritize GenAI brand visibility over keyword optimization, because keyword strategy is a thing of the past, while knowledge graphs will define your current and future brand visibility.”

When a page consistently references the entities most relevant to a subject — such as “content operations,” “CMS governance,” or “editorial planning” — search systems gain a clearer understanding of its place within a semantic neighborhood. These relationships help build topical authority by showing how concepts reinforce one another within a cluster.

Entity Mapping and Internal Linking

Entity mapping shapes internal linking by revealing which pages should connect through shared concepts. Connecting pages through shared entities reinforces the relationships the Knowledge Graph expects to see in a well-structured cluster. As HubSpot’s semantic search guide notes, structured relationships help search engines evaluate the depth and cohesion of a topic.

Entity-led planning improves editorial strategy by reducing duplication and clarifying where new content is needed. Topics such as “content audit frameworks,” “AI-assisted drafting,” or “internal content quality standards” may share overlapping keywords, but they represent distinct entities. Incorporating those entities into briefs and planning documents ensures each article contributes something unique to a cluster.

Entity-focused content also improves retrievability in AI systems, which rely on conceptual relationships to identify authoritative sources and reconstruct information. As large language models play a greater role in surfacing results, strong entity signals provide additional visibility beyond traditional SERPs.

How Entity SEO Works

1. Entity Detection

When a crawler reads a page, it identifies the entities present, such as the people, organizations, products, concepts, and places mentioned in the content. This happens through natural language processing (NLP). Natural language processing helps detect entities’ meanings and contexts.

A page about “email marketing platforms” might trigger entity detection for HubSpot, Mailchimp, automation workflows, and subscriber segmentation.

2. Context Analysis

Once entities are detected, the search engine evaluates the surrounding context to understand how they relate. It’s not enough for HubSpot to appear on a page — the engine looks at what else appears alongside it.

Is it mentioned in the context of CRM, marketing automation, or sales pipeline? That context shapes how the entity relationship is interpreted.

3. Knowledge Graph Matching

The detected entities are then matched against the Knowledge Graph — Google’s database of known entities and their established relationships.

If the page’s entity map aligns with what the Knowledge Graph already understands about a topic, the page earns relevance signals across the entire conceptual neighborhood, not just for a single keyword.

4. Relevance Evaluation

The engine evaluates whether the page contributes meaningfully to the topic. Depth matters here. A page that covers an entity thoroughly — defining it, connecting it to related concepts, and reinforcing those connections through internal links — earns stronger relevance signals than one that mentions the entity in passing. Internal links reinforce relationships between related entities and topics.

An on-page example: A blog post titled “How to Build an Email Marketing Strategy” doesn’t just need the phrase “email marketing strategy.” It should reference the entities that belong in that semantic neighborhood, things like:

• Subscriber segmentation
• A/B testing
• Automation workflows
• Deliverability
• Lead nurturing

Each of those entities is a signal that tells the Knowledge Graph this page understands the topic, not just the keyword. When search engines clearly understand what a page is about and how it connects to related concepts, visibility improves across entire clusters.

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How to Find Entities for SEO

To find entities for SEO, start with the concepts search engines already recognize: mine Google’s search features (Knowledge Panels, People Also Ask, related searches) and Wikipedia for established entity relationships, then expand with semantic analysis tools like Google’s Natural Language API, Ahrefs, or Semrush. The goal is to tap into the millions of interconnected concepts already in Google’s Knowledge Graph rather than inventing relationships from scratch.

Here’s a practical approach to discovering and organizing entities for any content strategy.

Step 1: Start with clear goals and core topics.

Every strong entity strategy begins with a simple question: What’s the main topic, and who needs to find it?

Marketing automation might be the core topic for a SaaS company, which naturally branches into related areas such as CRM integration, email workflows, and lead scoring. These aren’t random connections — they’re the actual problems and solutions that audiences search for.

HubSpot’s AEO Grader offers a reality check here, showing how AI systems currently interpret brand content across ChatGPT, Perplexity, and Gemini. AEO Grader analyzes brand presence in AI search using entity signals. It’s one thing to assume certain entity connections exist — it’s another to see what AI actually recognizes.

Where AEO Grader captures a snapshot, HubSpot AEO tracks how a brand shows up across answer engines over time, analyzes competitor presence, and delivers prioritized recommendations to strengthen visibility.

Step 2: Mine search results and Wikipedia for proven entities.

Google already shows which entities matter through search features. The “People also ask” boxes, Knowledge Panels, and related searches aren’t just helpful features — they’re a roadmap of recognized entity relationships.

Wikipedia deserves special attention since it feeds directly into Google’s Knowledge Graph. The blue links in a Wikipedia article’s opening paragraphs reveal entity connections Google trusts. An article about email marketing links to marketing automation, CRM systems, and open rates. Each link essentially says, “These concepts are related.”

Tools like Ahrefs and Semrush build on this foundation. Their analyses confirm which entities appear most frequently in top-ranking content, converting qualitative observations into measurable patterns.

Step 3: Expand entity maps with semantic analysis tools.

Once the core entities are clear, the next step is to identify the gaps and connections competitors might be overlooking.

Google’s Natural Language API

Google’s Natural Language API reads any piece of content and identifies which entities it contains — invaluable for checking whether existing content hits the right semantic marks.

Ahrefs and Semrush

Ahrefs and Semrush have evolved beyond keyword research, now offering entity recognition and semantic clustering that reveal how topics connect in the Knowledge Graph. Their content gap analyses specifically highlight entity opportunities that competitors rank for.

Clearscope and Surfer

Clearscope and Surfer analyze top-ranking content to surface the supporting concepts — related tools, people, and subtopics — that a page needs to cover thoroughly. That makes them useful for spotting which entities give competing content its depth.

HubSpot’s Nexus (Internal)

For HubSpot’s internal content teams, there’s also Nexus — a proprietary tool that’s transforming how the company approaches entity mapping.

Killian Kelly, AI search technical strategist at HubSpot, developed Nexus to bridge a critical gap between theory and operational reality. “I came up with the idea for Nexus after seeing how much attention vector embeddings were getting in the SEO and AEO space, but no one had a practical way to use them in real content strategy,” Kelly explains.

Nexus models how AI systems like ChatGPT and Google’s AI Mode interpret search intent, analyzing semantic relationships across entire content libraries. The tool generates topic scores revealing exactly which pages align with target entities and where coverage gaps exist.

“Nexus helps us visualize how topics, subtopics, and entities connect across our content,” Kelly notes. “We can run a key topic through Nexus and instantly see an overall topic score — along with which pages align semantically with that entity and which areas we’re missing altogether.”

HubSpot’s team runs key topics through Nexus monthly to:

• Evaluate semantic coverage across the cluster
• Identify competing pages that target the same entities
• Spot gaps where coverage is thin or missing

Those insights feed directly into content briefs, consolidation priorities, and pruning decisions.

The optimization feedback loop makes the impact measurable. Once the team fills gaps and strengthens coverage, they can return months later to see how topic scores have improved and whether entity signals have strengthened across the cluster.

Step 4: Build topic clusters around entity relationships.

With entities identified, the next step is organizing them into clusters that serve both search engines and readers. The strongest clusters map the relationships that already exist between concepts.

A strong cluster starts with a pillar page that covers a broad entity, such as “AI marketing.” The supporting pages then dive into specific aspects: AI content generation, chatbots for customer service, and predictive analytics for campaigns. Each piece reinforces the others through internal links and shared context, creating what search engines recognize as topical authority.

Keeping everything organized as content libraries grow presents a practical challenge. Content Hub addresses this through templated briefs and automated internal linking, maintaining consistency across dozens or hundreds of related pages. When every new article strengthens the overall entity map instead of existing in isolation, real authority builds.

Pro tip: HubSpot’s SEO recommendations tool makes this visual, showing exactly where internal links are missing between pillar and cluster content, turning abstract entity relationships into actionable improvements.

Step 5: Reinforce with structured data.

Schema markup helps search engines understand page entities and their attributes. While not mandatory for entity SEO success, schema acts like a translator — explicitly stating what each entity is and how it connects to others.

For a page about HubSpot Content Hub, schema tells Google exactly what’s what:

• “HubSpot Content Hub” is a software product.
• “HubSpot” is the organization behind it.
• “Entity-based SEO” is a topic covered within the content.

A simple JSON-LD example looks like this:

Free tools like Google’s Structured Data Markup Helper generate this code automatically, and the Rich Results Test confirms it’s working before publication. Done well, schema improves the chances of appearing in rich snippets, AI-generated answers, and knowledge panels — the high-visibility spots that drive real traffic.

Step 6: Master entity linking and disambiguation.

Finding the right entities is only half the job. The other half is making sure search engines — and AI systems — understand which entity a page refers to. The fix is to deliberately anchor content to authoritative references that the Knowledge Graph already trusts.

Here are a few ways to do this:

1. Reference authoritative sources explicitly.

Wikipedia and Wikidata are the most direct routes, since Google’s Knowledge Graph draws heavily on both. Linking to, or structurally mirroring, the naming conventions of a Wikipedia article signals which entity a page is about.

For example, if the Wikipedia article for “HubSpot” describes it as a CRM platform and marketing automation company, those are the entity descriptors worth reinforcing.

2. Use sameAs in schema markup.

The sameAs property in JSON-LD is one of the clearest disambiguation signals available. It tells search engines that the entity described on this page is the same entity recognized at an authoritative external source — a Wikipedia URL, a Wikidata identifier, or an official brand page.

A simple implementation looks like this:

{

"@context": "https://schema.org",

"@type": "Organization",

"name": "HubSpot",

"sameAs": [

"https://en.wikipedia.org/wiki/HubSpot",

"https://www.wikidata.org/wiki/Q5926631"

]

}

4. Reinforce meaning with related entities.

A page about marketing automation becomes easier for search engines to interpret correctly when it also references CRM integration, lead scoring, and email workflows. The keywords themselves don’t need to appear. What matters is that those entity relationships confirm the semantic territory the page belongs in.

Related entities act as context clues that resolve ambiguity and simultaneously strengthen relevance signals.

Implementation Signals at a Glance

When these signals work together, search engines don’t have to guess what a page is about.

How to Plan Topic Clusters With SEO Entities

To plan topic clusters with entities, start with a broad pillar entity, then map supporting subtopics that share context and link them together. Entities anchor these clusters, linking related ideas through shared context, internal linking, and consistent topical framing.

Effective clusters mirror how people research subjects: beginning with a broad concept and moving into increasingly specific subtopics. Entity relationships naturally guide this progression by showing which concepts belong together and how deep each area should go.

Here’s what effective entity-based clustering looks like in practice:

Clusters become most useful when they directly inform content creation. Each entity turns into a content opportunity with clear intent and a defined set of internal links. For example, a page about email sequences naturally connects to A/B testing, lead nurturing, and the broader marketing automation pillar. These connections follow patterns that readers expect and search engines reward.

Operationalizing this structure at scale means turning entity insights into reusable brief templates and maintaining editorial consistency across expanding content libraries, so whether the output is a blog post, case study, or video, each piece strengthens the broader entity map.”

Clusters also help identify gaps. When competitors rank for entity relationships missing from existing content, those gaps become a built-in roadmap for future editorial planning and quarterly content development.

Pro tip: Check out these SEO best practices for more tips and strategies.

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How to Measure and Report on Entity-based SEO Strategy

Measuring entity-based SEO focuses on whether search engines recognize and reward topical authority across related concepts, not on the performance of individual keywords.

The strongest indicators show growth across clusters, improved semantic coverage, and greater visibility in the SERP features that rely on contextual understanding.

Track cluster-level performance in Google Search Console.

Google Search Console provides the most direct view of entity-led progress. Instead of isolating keyword-level queries, monitor impressions and clicks across entire clusters of pages tied to a shared concept. Rising visibility across these interconnected pages signals that Google understands the entity relationships and is treating the site as an authoritative source within that domain.

Evaluate internal link density and relationship mapping.

Entity-rich sites demonstrate tight internal linking between related topics. As clusters grow, the density and consistency of these links help search systems understand how concepts reinforce each other. HubSpot’s Tools that automatically surface related pages and suggest internal links help ensure supporting content connects back to pillar pages and relevant subtopics. Over time, this creates a semantic network that signals depth and authority.

Monitor SERP features influenced by entity clarity.

Entity-optimized content is more likely to appear in featured snippets, knowledge panels, and AI-generated answer boxes — all of which rely on structured context rather than keyword matching. Increases in these placements show that search engines can clearly interpret the page’s meaning and its relationship to other concepts.

Connect entity performance to engagement and outcomes.

Entity authority often correlates with stronger behavioral metrics. As clusters mature, rising impressions typically accompany higher engagement, longer time on page, and more consistent conversion paths. When search systems understand the relationships between topics, the content surfaces in more relevant contexts — driving better downstream performance.

Use AEO Grader for emerging visibility signals.

HubSpot’s AEO Grader adds a forward-looking dimension by showing how a brand appears across AI-driven search environments such as ChatGPT, Gemini, and Perplexity. These insights help determine whether entity signals are strong enough for LLM-based retrieval and where additional semantic reinforcement may be needed.

Measure Entity Salience and Knowledge Graph Presence

Traditional SEO metrics indicate whether content ranks, while entity-specific KPIs indicate whether search engines understand it. As entity-based strategies mature, these signals become the clearest indicators of whether semantic authority is actually building.

Track these alongside standard performance metrics:

1. Entity Salience Score

Google’s Natural Language API assigns each detected entity a salience score between 0 and 1, reflecting how central that entity is to the content.

A high salience score for a target entity — say, “marketing automation” on a page built around that concept — confirms that the content is interpreted as intended. Low salience on a target entity signals that the page isn’t communicating its focus clearly enough.

2. Entity Coverage Across a Cluster

Audit which target entities appear consistently across pillar and supporting pages. Gaps in entity coverage represent concrete opportunities to strengthen semantic authority.

Tools like Clearscope and Surfer surface these patterns at scale.

3. Knowledge Graph Visibility

Monitor whether target entities trigger Knowledge Panels, branded search features, or entity-linked SERP features. Growing Knowledge Graph presence, especially for brand or product entities, signals that Google has enough confidence in the entity to surface it proactively.

4. AI Search Presence

Track how consistently the brand appears in AI-generated answers across ChatGPT, Perplexity, and Gemini for target topics.

HubSpot’s AEO Grader scores this, showing entity strength across AI environments and flagging where additional semantic reinforcement is needed.

5. Entity-Driven SERP Feature Wins

Featured snippets, People Also Ask inclusions, and AI Overview citations are entity-driven placements. Tracking gains in these features — separately from standard ranking movement — shows whether entity clarity is translating into the high-visibility positions that AI-era search rewards.

Tools and Reporting Context

The right tools make entity measurement practical, but they work best when used alongside traditional SEO reporting, not instead of it.

• Google’s Natural Language API is the easiest way to evaluate entity signals on any page. Paste in content, and it returns detected entities, salience scores, and entity types — giving a clear picture of whether a page’s semantic focus matches its intent. It’s particularly useful for auditing existing content before optimization and for verifying that updated pages are being interpreted correctly.
• Ahrefs and Semrush provide entity-adjacent signals through their semantic clustering, content gap, and topical authority features. While neither tool reports entity salience directly, their analyses of top-ranking content reveal which entity relationships competitors are reinforcing — and where coverage gaps exist.
• Clearscope and Surfer surface the supporting entities that give top-ranking content its topical depth, making them useful for brief-level entity planning and post-publication audits.
• HubSpot’s AEO Grader adds the AI-visibility layer, showing how brand entities appear across AI-driven search environments and where entity signals need strengthening for LLM-based retrieval.

Pro tip: Entity-specific KPIs are designed to complement traditional SEO metrics, not replace them. Rankings, organic traffic, click-through rate, and conversions remain the primary indicators of content performance. Entity metrics add a diagnostic layer, helping to explain why content is performing the way it is and where semantic gaps are limiting growth.

When used to complete an enterprise SEO audit, they give a more complete picture of both current performance and future opportunity.

Frequently Asked Questions About Entity-based SEO

Are entities the same as keywords?

No. Entities differ from keywords in that they have context and relationships. Keywords are text strings that reflect how people search, while entities are the underlying concepts that those strings refer to. For example, “CRM platform” is a keyword; HubSpot is an entity representing a specific product and organization. Entities help search systems understand meaning and context rather than matching text alone.

Do I need schema to benefit from entity SEO?

Schema markup is helpful but not required for entity SEO. Schema markup disambiguates entities for search engines. It provides explicit, machine-readable definitions of the entities on a page and how they relate to one another. Schema increases clarity for search engines and often improves visibility in featured snippets, knowledge panels, and AI-generated summaries.

How do I find related entities for my topic?

Tools such as Google’s Natural Language API, Ahrefs, and Semrush surface entities commonly associated with a primary concept. Wikipedia, People Also Ask panels, and related searches also reveal trusted entity connections. Internal linking further reinforces those relationships by mapping how concepts support one another within a cluster.

How do entities affect rankings?

Search engines identify entities with natural language processing. When these platforms, like Google, recognize strong entity coverage, visibility improves across multiple related queries rather than just one term. Entity-driven pages often show consistent growth across entire clusters because search systems understand how each piece fits within a broader topic.

What’s the best way to measure entity SEO results?

Monitor impressions, clicks, and ranking trends for entity-aligned clusters in Google Search Console. Track internal link development and SERP feature visibility to assess whether semantic authority is increasing.

How can I make my content more AI-friendly using entities?

Clear definitions, consistent naming conventions, and structured internal links make entity relationships explicit for AI models. Breaking up dense paragraphs, using schema markup where appropriate, and maintaining consistent terminology across assets improves machine interpretation.

What is the difference between entity SEO and semantic SEO?

Entity SEO and semantic SEO overlap significantly, but they aren’t the same thing. Entity SEO focuses specifically on identifying, clarifying, and strengthening the discrete concepts — people, organizations, places, products — that search engines recognize as distinct objects within the Knowledge Graph. Semantic SEO is broader, and it covers the full range of strategies that help search engines understand meaning, including topic coverage, search intent, and contextual relevance.

How do entities affect AI search and ChatGPT rankings?

AI systems like ChatGPT, Perplexity, and Gemini don’t rank pages the way traditional search engines do — but entity signals directly influence whether content gets surfaced, cited, or used to construct an answer. When a page clearly establishes which entities it covers, how those entities relate to one another, and where the brand fits within a semantic neighborhood, AI systems can more confidently retrieve and reference it. Strong entity clarity doesn’t guarantee citation, but it removes the friction that keeps well-written content from being recognized as authoritative in AI-driven environments.

Shift from keywords to entity-based SEO.

Entity-based SEO reflects how modern search engines interpret content through context and relationships. When those relationships are clear, visibility improves across both traditional search and AI-generated experiences.

Content Hub makes this structure scalable by identifying entities, templatizing briefs, and maintaining semantic consistency across large content ecosystems. AEO Grader shows how entity signals perform in AI environments such as ChatGPT, Perplexity, and Gemini — visibility that’s increasingly important as search continues to evolve.

The shift from keywords to entities changed my approach to content strategy. When clusters formed around natural relationships rather than isolated terms, it became clear why Google rewards content that connects ideas. The strongest performers were those who demonstrated how concepts relate.

As AI plays a bigger part in information retrieval, building content around entities ensures long-term visibility and credibility. The goal extends beyond ranking for individual queries, and it centers on producing content that earns authority through real expertise and clear semantic structure.

Editor's note: This post was originally published in January 2025 and has been updated for comprehensiveness.

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