Entity-Based SEO

TL;DR: Entity-based SEO is the practice of organizing your brand’s data so search and AI engines can clearly understand what your business is, what it offers, and how it’s connected to people, places, products, and services.

Search used to be about matching keywords to web pages. If you used the right terms, you could rank high on Google and be found by customers fairly easily.

But today, search and AI engines don’t just look at words. Instead, they try to understand what your business actually is and how it connects to other things in the real world.

That shift means it’s not enough to optimize for keywords alone. You need to make your brand’s information clear, consistent, and machine-readable everywhere it appears across the web.

Entity-based SEO is how brands make that happen.

What is entity-based SEO?

Entity-based SEO (also known as entity SEO) is a search optimization approach that focuses on how search engines and AI engines understand the world — not as strings of keywords, but as a structured network of real-world things and the relationships between them.

Traditional SEO asks: What words are people searching?

Entity-based SEO asks: Do search and AI engines understand what my brand actually is, what it offers, where it operates, and how it relates to other entities in SEO?

As AI answer engines become more common, entity-based thinking is no longer just an advanced SEO strategy. It’s now something every brand needs if they want to show up in AI-generated answers and succeed in answer engine optimization, generative engine optimization, and AI search optimization.

What is an entity in SEO?

In SEO and AI, an entity is a specific, identifiable thing in the real world that can be defined by its attributes and linked to other things through relationships.

Entities can be:

• A brand or business (Yext, a local restaurant, a healthcare provider)
• A location (a specific store or branch)
• A person (a doctor, a financial advisor, an author)
• A product or service (a specific menu item, a software plan, a treatment offered)
• A concept (a topic, a category, a standard)

What makes an entity different from a keyword is specificity and structure. “Coffee shop” is a keyword. A specific coffee shop at a specific address with specific hours, a specific menu, and a specific set of customer reviews is an entity.

Search and AI engines model the world using entities because they carry meaning and context that keywords alone can’t provide. This is a core idea behind semantic SEO and how modern search works.

What are entity relationships in SEO?

Entity relationships are the connections between entities that give meaning to the overall structure. They answer questions like: What does this brand offer? Where is it located? Who provides the service? What category does it belong to?

Examples of entity relationships in SEO include:

• A brand entity has a relationship to a location entity ('Yext has an office at 61 Ninth Ave, New York')
• A location entity has a relationship to a service entity ('this branch offers wealth management consultations')
• A person entity has a relationship to an organization entity ('Dr. Smith works at Memorial Health Clinic')
• A product entity has a relationship to a category entity ('this item is a seasonal appetizer on the dinner menu')

These relationships are how search engines build rich, contextual understanding of your brand, and how AI engines decide whether to include you in an answer to a specific, contextual query.

How search engines use entities

Google and other traditional search engines have used entity SEO and knowledge graph SEO modeling for over a decade. In 2012, Google launched their very own knowledge graph — a database of entities and their relationships that powers features like Knowledge Panels, rich snippets, and local search results.

When you search for a business, Google doesn't just match keywords. It retrieves an entity from its knowledge graph and surfaces the attributes and relationships it has confidence in: the name, address, phone number, hours, category, reviews, associated people, and related services.

For brands, this means the question isn't just “Do I rank for this keyword?”, but “Does Google understand what I am, what I offer, and how I'm connected to the things my customers are searching for?”

How AI answer engines use entities

AI answer engines like ChatGPT, Gemini, and Perplexity take entity modeling even further. Understanding how AI search engines work is key: they synthesize answers from multiple sources, and those answers are built on their understanding of entities and the relationships between them.

When someone asks an AI engine “What's the best physical therapy clinic near downtown Austin?”, the AI isn't searching for pages that contain those words. It's querying its model of the world by asking questions like:

• Which entities of type 'physical therapy clinic' are associated with downtown Austin?
• What attributes and reviews are associated with those entities?
• Which ones have enough structured, consistent data to cite confidently?

This is why entity clarity — having clean, complete, consistent structured data about your brand's entities and their relationships — is directly tied to brand visibility and how to optimize for AI search.

How to signal entity relationships to search and AI engines

There are a few key ways to show search and AI engines what your entities are and how they’re connected:

Structured data markup: Adding Schema.org and structured data markup to your web pages explicitly tells engines what type of entity each page represents and what its attributes are. Schema types like LocalBusiness, Person, Product, and Service create machine-readable entity definitions.

Knowledge graph / centralized data: Storing your entity data in a centralized, structured knowledge graph helps make sure that your brand's entities and relationships are consistently defined across all the data you distribute, everywhere it’s being distributed.

Business listings and directories: Consistent, accurate listings on Google Business Profile, Apple Maps, Yelp, and other directories act as corroborating sources that reinforce entity attributes and signal trustworthiness. This also includes consistent NAP data: name, address, and phone number consistency across the web reinforces location entity accuracy and prevents conflicting signals that confuse engines.

Internal linking: Deliberate internal link structures on your website help engines understand how your entities connect — linking location pages to service pages to people pages creates a web of relationship signals.

Co-citations: When third-party sources mention your entity in the same context as other trusted entities, it reinforces the engine's understanding of your relationships and category.

The role of a knowledge graph in entity-based SEO

A knowledge graph is a database designed to store entities and their relationships in a structured, queryable format. It's how both Google (at the search engine level) and brands (at the data management level) model the real world.

For brands, maintaining a knowledge graph means having a single, authoritative record of every entity you manage — locations, products, services, providers — with all their attributes and relationships clearly defined. This centralized structure is what makes it possible to consistently communicate those entities to external search engines and AI models.

Without a knowledge graph, brand entity data tends to live in silos: location data in a CRM, product data in a catalog, people data in an HR system. When it's fragmented, it's nearly impossible to present a coherent, consistent entity picture to AI engines — which is exactly what those engines need to confidently include you in answers.

How Yext supports entity-based SEO

Yext is built around the concept of entities. Our purpose-built Knowledge Graph makes it easy for multi-location brands to model all their entities with structured attributes and explicit relationships between them.

That entity data then flows outward to:

• Local pages and website content — structured, Schema.org-marked pages that help AI engines identify and understand your entities
• Listings across 200+ publishers — consistent entity data distributed broadly so AI engines encounter the same accurate facts everywhere they look
• Reviews and social signals — rich context that adds subjective credibility on top of your structured entity facts

The result is a brand whose entity data is not just accurate in one place, but coherent and consistent across the entire web — exactly the signal AI engines use to decide who to trust and cite.

Explore Yext’s Knowledge Graph and take control of how your brand is understood across search and AI.