Semantic SEO: How to optimize for meaning over keywords

 Semantic SEO helps search engines understand context. Learn how to use entities, topics, and intent to build richer content that ranks higher.

Semantic SEO aims to describe the relationships between entities so that Google, along with newer AI answer engines, can understand the content on a website.

While semantic SEO is hardly new, it now helps bridge a critical gap between traditional SEO and newer generative engine optimization (GEO) and AI optimization (AIO) efforts.

Learn why semantic SEO matters, see how search engines and AI visibility tools use semantic understanding to power their generated results, and find out how you can optimize your content in this entity-driven environment.
Why semantic SEO matters now

Semantic SEO is more important than ever because it helps ensure your content appears in relevant contexts: traditional Google search engine result pages (SERPs) and newer generative engine results like Google’s AI Overviews and ChatGPT prompt responses.
Semantic Influence

It’s no longer good enough to simply target keywords. Long ago, Google started shifting from relying on keywords to using topics and entities to drive search results.

The relationships between keywords, topics, and entities are the semantic relevance provided by helpful, user-focused content.

At a high level it works when Google indexes websites, parses the entities it finds, and stores information about those entity relationships in its Knowledge Graph. Then, when someone submits a search query, Google uses natural language processing (NLP) to understand the search intent, retrieve the information, and present it as a combination of SERP features.

Without semantic SEO, this search engine rankings process would be much less effective. The semantic meaning provided by structured data and other markup that ensures Google understands your content correctly throughout the whole process.

For a more in-depth explanation of this process and its impact, see How search engines use semantic understanding below.
What is semantic SEO?

Semantic SEO is the process of optimizing content for meaning, context, and relationships between entities. 
Development

The word “semantic” relates to the meaning of words. Thus, “semantic SEO” is search engine optimization that specifically deals with the definitions and intents of the content on a website.

In the early days of SEO, using a keyword enough times on a page (keyword stuffing) could get content to rank—regardless of whether that keyword had any connection to the topic of the page.

As Google tried to crack down on keyword stuffing, it tried to better understand the main topic of a page. To do so, it began to rely on other ranking signals to identify that topic.

Relying on topical relevance was better than simple keyword matching. But it still excluded content from results that might be relevant to the search simply because it didn’t use the right words and phrases.

For Google’s algorithms, search intent relevance is much more important than keywords and topics. That’s where semantic SEO comes in.

Semantic SEO goes far beyond employing individual keywords and topical phrases. Rather, it uses schema markup and other structured data to describe the relationships between these entities:

    People
    Places
    Objects
    Concepts and ideas
    Data and facts

When it understands the relationships between these entities, Google can serve results that better relate to the user’s search—even when the search terms don’t exactly match keywords within the content.

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A semantic SEO example: Apple or apple?

Imagine you submitted a Google search for “apple.”

Google would have to do a little work to figure out exactly what you mean:

    The company Apple (Apple, Inc.) that makes popular products like MacBooks, iPhones, and EarPods
    The fruit apple that comes in many varieties like Cortland, Gala, and Red Delicious
    Some other technical or derivative reference, such as a color, botanical concept, or a phrase like “Adam’s apple”

Semantic Seo

After a little thinking, Google will reason that you’re probably looking for the company and serve you results like the following.
Google Serp Apple Scaled

However, if you make the search plural (“apples”), it will likely give you results that focus on the fruit.
Google Serp Apples Scaled

If Google based its search results solely on keyword usage, then it would probably deliver mixed results for both search terms.

    “apple” would include pages about the fruit
    “apples” would include content from the company

In fact, if you look at older versions of Google, you’d see results like the one below that include results for both the company and the fruit.
Wayback Google Serp Apples Scaled

Today, Google is mostly able to avoid this sort of ambiguity by understanding what entities the searcher wants to know more about.

This is why from a content marketing perspective, it’s important to clearly identify entities and provide coverage beyond just the primary topic or two you want to focus on.

That’s where the power of semantic SEO comes in.
How search engines use semantic understanding

Search engines like Google use semantic understanding to discover how entities are connected to each other across web content. They then provide semantically relevant search results to users. 

Google’s process has three basic phases:

    Google indexes website content, identifying entity relationships from the content’s structured data and storing them in its Knowledge graph
    When a user submits a search, Google parses it using NLP tools to understand the user’s intent behind the search
    Google then serves a packaged set of search results with various features like AI Overviews, a Knowledge Panel, and search listings

A lot happens during each of these phases, so let’s take a closer look.
Google Knowledge Graph and entity indexing

The Google Knowledge Graph is a gigantic database that stores information about relationships between different entities (i.e., people, places, objects, concepts, and data).

To understand what that means, it’s useful to know what a graph is, what a knowledge graph is, and how it applies to semantic SEO.

A graph is a way of modeling paired data. It uses statements to describe relationships between entities. Graphs have a lot of different applications, such as describing the different nodes of a network. 

A knowledge graph describes how different entities relate to each other semantically. That is, it describes the context and meaning between the entity relationships.

For example, the statement “Apple is a company” has three parts:

    Subject: Apple
    Predicate: is a
    Object: company

The semantic meaning of this relationship is in the predicate “is a,” which describes “Apple” as a type or instance of the more general entity “company.”
Semantic Relationship

Knowledge graphs can get much more complex, but they’re all constructed from the three main building blocks of subject, predicate, and object.

The Google Knowledge Graph works essentially the same way. 

Google gathers information about entity relationships from multiple sources, including:

    Public domain sources like certain government publications 
    Open or community data like Wikipedia and Wikidata
    Private licensed data like sports scores and stock market prices
    Websites crawled and indexed by Googlebot

It processes and stores relationship data about various entities in its Knowledge Graph. Then, it serves the information in search results in AI Overviews, Featured Snippets, Knowledge Panels, and other SERP features.

For websites, Google encourages using structured markup to describe the relationships between entities. (See Schema markup below for more information on how to do that.)

However, Google goes beyond just looking at structured data on websites. It also uses NLP algorithms to understand and extract information about entities.
NLP advancements: RankBrain, neural matching, BERT, MUM, and embeddings

The prevalence of NLP in Google’s search algorithm allows it to understand the meanings and uses of words and phrases far beyond simple keyword detection.
Nlp Timeline

Google has used NLP in various ways since about the mid-2000s (possibly earlier):

    2006: Google Translate launched. Often overlooked in discussions of AI in search, Google offers machine translated search results powered by its own translation tool.
    2015: RankBrain introduced as a method of better interpreting search queries
    2018: Neural matching allows understanding of search queries without using exact keywords.
    2019: Previously launched as an open source NLP model, Bidirectional Encoder Representations from Transformers (BERT) applied to Google Search
    2021: Multitask Unified Model (MUM) improves 1,000 times on BERT to understand and generate language
    2023: AI Overviews start to appear, powered by the Gemini large language model (LLM) as part of Google’s Search Generative Experience (SGE)

Each of these steps in the advancement of NLP has pushed Google’s capabilities into new territories, targeting different steps of the search process.

Underpinning these AI advancements is a machine learning concept known as “embeddings.”

Without getting too technical:

    Embeddings are representations of categorical data using vectors
    Vectors are arrays of encoded numbers that list individual elements (“features”) within a category
    Encoding is the process of converting high-level data (such as a word or image) into a number that can be used in vectors. Embeddings use one-hot encoding.

Embeddings are used to calculate the closeness of different features to each other across an arbitrary number of vectors—which can be high.

Pro tip: A deep understanding of embeddings, vectors, and related machine learning concepts goes far beyond the scope of this guide. Google offers a free Machine Learning Crash Course that provides a more in-depth explanation.

When it comes to search, embeddings power a host of NLP applications. A few of these are: 

    Synonyms, related terms, and correlated concepts in queries
    Autocomplete suggestions and predictive text
    Similar products, brands, or locations
    Image analysis and optical character recognition (OCR)

For example, embeddings help Google understand:

    Apples are food, and more specifically fruit
    Semantic relationships between the general concept of apple and specific varieties like Granny Smith, Golden Delicious, or Jonagold
    More distant connections between physical apples and metaphorical concepts like “the apple of my eye”

With the introduction of LLMs like Gemini, these machine learning concepts and NLP models go a step further to provide even more useful results.
AI Overviews and entity recall in generative results

Google’s expansion of AI Overviews and AI Mode has increased the value of semantic SEO greatly, especially when it comes to brand visibility. This is due in large part to the concept of entity recall. 

Entity recall refers to the ability of LLMs like Gemini, Claude, and ChatGPT to reference a particular entity in its generated response.

In an ideal scenario, entity recall should follow several principles:

    Correctness: Only entities relevant to the question or prompt should be included
    Completeness: Relevant entities that fit the criteria of the search or prompt should not be excluded
    Consistency: Submitting the same question or prompt should recall the same entities

In reality, there are often additional parameters built into AI models, such as limiting the number of entities referenced in a response or preferring more recent training data over older data.

This can cause additional problems to crop up with entity recall:

    Hallucination: Misunderstanding the semantic relationships between entities and misreading queries or prompts can lead to incorrect or outright bizarre responses
    Incompleteness: Knowing what data to include or exclude becomes an exponentially more complex process as the amount of data used to seed LLMs increases.
    Staleness: Updating training data can be a hefty task that grows heftier the more data that’s collected. As processes to update data evolve, this may get better. But it may also lead to other problems like knowing when to provide current versus historical information.
    Misinformation: A growing body of spam targeting LLMs can lead to inaccuracies, undercutting the strides Google has taken over the decades to reduce spammy content in search results.

To ensure your brand shows up in AI Overviews, it’s important to consider all the implications of these entity recall problems.

The best way to do that is to provide as much information as possible to Google and other LLMs about your brand as an entity and its relationships to other entities.

The way you do that is with semantic SEO.
Why entity-first indexing is reshaping SERPs

By understanding entities rather than just keywords, Google is able to build a much richer set of search engine results. It’s been doing so for years.

Entity-first indexing is a concept first introduced by Cindy Krum as a way to reframe mobile-first indexing. 

Essentially, entity-first indexing reinforces how Google collects and organizes information in its Knowledge Graph and elsewhere. The concept is built on several components:

    Google’s mission is “to organize the world’s information and make it universally accessible and useful”
    Crawling and indexing are different steps in Google’s attempt to understand and organize information on the web
    As such, mobile-first indexing is misnamed because it actually refers to the way Google crawls webpages, not how it indexes them
    The indexing step focuses on understanding the entities (people, objects, concepts, etc.) covered on the page and their relationships to other entities

Based on these components, entity-first indexing better describes how Google processes information for later retrieval.

Note: The idea of entity-first indexing is not intended to dismiss the importance of mobile user experience (UX). Mobile optimization is still important, as the mobile version of a webpage is the one Google indexes.

Entity-first indexing explains how Google uses paired entity data to create entity-oriented search results.

In fact, some search results are more filled with entity-based SERP features than traditional search listings, at least on the first page.

Here are some of the most prominent entity-oriented SERP features:

    AI Overview: These increasingly in-depth AI-generated explanations are generated from entity-based data. (See “AI Overviews and entity recall” above for more on this topic.)
    Knowledge Panels: These provide a host of quick-hit information powered by entity relationships pulled directly from Google’s Knowledge Graph. They often denote the specific relationship of the entities like a company’s founding date or a fruit’s nutritional information.
    Things to Know: This newer SERP feature provides quick facts and information about the subject of the search, often in the form of AI Overviews or featured snippets
    People Also Ask (PAA): Questions that appear in PAA sections offer deeper insights into specific aspects of the initial search. They generally include AI Overviews or Featured Snippets with entity-specific data pulled from the Knowledge Graph.
    People Also Search For (PASF): These SERP features prompt users to search for related or similar entities, such as companies in the same industry, foods with similar nutritional value, or people known for similar achievements like politicians or actors
    Top Stories: Recent news results, reviews, blog posts, and other timely articles require Google to identify the entities discussed in those articles and extract factual information, as well as metadata about the articles (publisher, author, date created or modified, etc.)
    Latest From: Similar to Top Stories, the Latest From feature includes official communications from a business—such as press releases, official news, blog posts, social media posts, and videos published by the company. All of these are identified by relating the entity of the initial search with its owned and managed assets.
    Popular Products: This transactional-intent SERP feature is based not on the Knowledge Graph but rather on Google’s Shopping Graph, which includes paired-relationship data from Google Merchant Center
    Places: These provide local results even when the query isn’t a “near me” search. If the entity is a business, the places listed might be retail locations (e.g., Apple stores), while searches related to other entities might include places to buy that item (such as grocery stores or apple orchards).

The above is just a small list of the different types of entity-oriented SERP features available. Google is continually testing and tweaking features, and new ones will continue to emerge as search becomes better at understanding entity relationships.
Core elements of semantic SEO

There are four core areas to consider with regard to semantic SEO:

    Entities, attributes, and values
    Topical authority
    Contextual relevance
    Schema markup

These all build on standard SEO best practices.
1. Entities, attributes, and values

When working with semantic SEO, semantic triples are expressed using the Entity-Attribute-Value (EAV) model, rather than the Subject-Predicate-Object model. (See Google Knowledge Graph and entity indexing for examples of the latter.)

The three parts of the EAV model are:

    Entity: The person, place, thing, concept, etc.
    Attribute: An associated property or entity type
    Value: The specific name of the property or entity

Eav

An entity will often have multiple attribute-value pairs. 

For example, the entity “apple” might have the following attribute-value pairs:

    Apple:
        Cultivar: Granny Smith
        Color: Green
        Status: Ripe

Furthermore, attributes themselves can be entities.

For example, the entity “basket” might contain more than one apple:

    Basket:
        Apple:
            Cultivar: Granny Smith
            Color: Green
            Status: Ripe
        Apple:
            Cultivar: Red Delicious
            Color: Red
            Status: Overripe

The benefit of expressing semantic relationships in this way is that it’s easy to adapt into a format that computers can parse.

The downside is that thinking about entity relationships in this way takes practice. For some people, it can feel less natural than the Subject-Predicate-Object method.

But if you’re able to shift your thinking to the EAV method, then you’ll be in good shape when it comes time to implement schema markup.
2. Topical authority

Semantic SEO involves building authority around relevant topics to reinforce your brand’s association with those topics.

In other words, the more you demonstrate authority in a given topic area, the more likely your brand is to appear in searches related to that topic.

The good news is that topical authority is something you can build over time. But to do it right, you need to be deliberate. Producing high-quality content is not enough; you need to be intentional with how you structure your topic clusters and content pillars.

Here’s a high-level overview of how to start building authority in a topic area:

    Develop a forward-looking content strategy that focuses on topics where you’re already an expert and have experience
    Make sure the topics align with your brand, products, and services
    Map out your content structure with a pillar and cluster model. (See Content clustering below for more on this technique.)
    Match content to user queries and user intents to ensure you cover every stage of the customer journey
    Create evergreen content that will stand the test of time
    Prune or update content that doesn’t meet performance standards

Keep in mind that authority also refers to the third element in experience, expertise, authority and trust (E-E-A-T).

Authority is very difficult to achieve without experience and expertise. In fact, brands often gain authority by demonstrating experience and expertise, such as through testimonials, awards, certifications, and other recognitions.

This means topical authority also requires topical expertise and topical experience. That’s why the first step in this process is to focus your content strategy on topics where you’re an experienced expert.

Trust comes in once you achieve the other three aspects of E-E-A-T. It’s the glue that holds them all together.

Again, the goal with topical authority is to reinforce connections between your brand and relevant topics. This can take time. But when you put in the effort, the results will be worth it.
3. Contextual relevance

Creating content with contextual relevance is important for aligning it with search intent and the entity relationships you want to highlight.

Contextual relevance differs from topical authority in a key way:

    Topical authority looks broadly to make sure you’re building content around the various products, services, user bases, and other things related to your brand
    Contextual relevance focuses on a specific page of content to make sure it includes all of the entities associated with the topic of that page

One of the ways NLP models understand context is through embeddings. (See NLP Advancements for a description of embeddings.)

Contextual embeddings specifically help AI distinguish between different definitions, connotations, ideas and—of course—contexts.

In other words, contextual embeddings let Google understand when your content is about “Apple” the company rather than “apple” the fruit. (Or vice versa.)

It does this by looking at what other entities are nearby:

    If it’s surrounded by words like “brand,” “tech,” “stock price,” and “iPhone,” then “Apple” probably refers to the company
    If it’s surrounded by words like “tree,” “nutrition,” “peel,” and “pie,” then “apple” probably refers to the fruit

Note: These examples use capitalization differences for easy reading. However, capitalization isn’t always a reliable way to distinguish between different uses of the same word. Contextual relevance is a better indicator of the entity being referenced.

Signaling contextual relevance requires ensuring the entities mentioned relate to each other in the way you want them to. When writing and editing your content, consider the following points:

    Place brands, trademarks, product names, and similar core business terms near the features, benefits, user problems they solve, or ideas they most closely represent
    Remove words and phrases that don’t add relevant value to the entities on the page
    Vary phrasing and word choice to show entities in different contexts and connotations. An example would be to call Apple (the company) a “business,” “company,” “tech giant,” and “industry leader” in different spots—each of which relates a similar concept with slightly different connotations.
    Define industry terminology and explain how it applies your own business offerings to connect your brand to the wider use of those terms
    Use semantic keywords judiciously to provide the right hints about the search intent of the page. (See Semantic keyword research below for how to find relevant keywords.)

From a semantic SEO perspective, the end goal is to make sure that the content reflects the entity relationships you want represented.

Of course, always remember that the ultimate goal of any content is to be helpful to users.
4. Schema markup

Schema markup is structured data that reinforces semantic signals. It’s used in the backend code of a webpage, so it isn’t something that users will see.

But Googlebot and other crawlers definitely see it.

Schema (as it’s often shortened) is a rigorous way of marking up EAV relationships. It uses vocabulary developed by Schema.org, a collaborative project between Google, Microsoft, Yahoo!, and Yandex.

You can implement schema using different methods, but JSON-LD is the most common one. It’s also the one recommended by Google.

JSON-LD uses JavaScript code to create attribute-value pairs for entities. It’s somewhat easy to read and understand for beginners, though it may take time to learn all the different types of entities and values that exist.

For example, consider this snippet of schema from the Apple.com homepage:
Script

Within the <script></script> tags and brackets ({}), the paired data reads as follows:

    @context indicates the vocabulary being used (schema.org)
    @id is a unique identifier for the entity
    @type is the type of entity being described (entity types are defined by the Schema.org documentation)
    name and url are properties, with the “…” indicating additional properties that can be included

Google supports a large list of entities that you can include in schema markup. Many of these describe information that Google may pull into the Knowledge Graph and include in SERP features.

For example:

    Breadcrumbs can appear in search result listings under the page title
    Review information can show up for products, services, organizations, media, etc., including ratings and snippets
    Event details can appear when searching for performers, venues, classes, or things to do
    Job postings can provide those looking for employment with information about career opportunities

These are just a few of the ways that schema markup can influence search results. A big part of semantic SEO involves identifying the features you want to appear in the search, and then incorporating the right schema into your page.
Strategies for implementing semantic SEO

Implementing semantic SEO involves considering your content and its underlying code from an entity-first indexing perspective. Each of the following techniques provides general recommendations to consider.
Entity optimization

Optimize entity relationships by exploring search results like the ones you want to appear in.

The first step is to do some research:

    Learn how to track entity presence in the Knowledge Graph
    Identify the entities you want your website and brand associated with
    Look at Knowledge Panels for searches similar to those you want to rank for and note the attribute-value pairs that appear
    For those same searches, look at what other SERP features show up and see how they map to Google’s guidance on structured data

Once you’ve identified the entities you want to optimize for, you’ll need to implement the appropriate schema.

Don’t just stop with high-level entities like Organization, Product, Person, and FAQs. Put as much information as makes sense for each type of content you publish. 

For example:

    If you allow customer ratings and reviews, use review snippet schema
    If you have a careers page and list open positions, use the job posting schema
    If you offer training and certification classes, use the course list schema

Review all of Google’s supported structured data markup to see which pieces fit the types of content you publish.
Content clustering

Content clustering involves creating pillar pages about broad topics, and then supporting them with clusters of pages on more specific subjects related to that topic.

The types of related topics you can create content clusters around include:

    Problems your brand helps to solve
    Types of products or services you offer
    Ways your brand pushes your industry forward (e.g., research and development, thought leadership, etc.)
    Strategic partnerships, such as joint ventures with other brands
    Community involvement

When building out your content strategy, keep in mind that pillars are not silos.

In fact, it is totally expected that your content creation will generate pieces of content spanning multiple pillars.

Consider how the entities at the core of each broad topic relates to other entities you’re discussing. You can approach this in several ways:

    Site structure: How do the physical layout and URL structure of your site signal connections between different topics?
    Navigation: How can you reinforce entity relationships through menus, breadcrumbs, footer links, and other navigational tools?
    Internal linking: Are you using correct and consistent anchor text to link between pages within clusters and across topics?

Clustering is about covering the right topics, not merely building content that focuses on specific keywords.

With that in mind, let’s talk about keywords.
Semantic keyword research

Semantic keyword research involves looking at the meanings of the words people search for, not just the general intent of the search.

When looking at related keywords to target, expand your efforts to include terms that can all be covered under the same topic.

Semantic keyword considerations for “apple” (the fruit) might look like the following:

    Synonyms: “Apple” might not have a lot of synonyms beyond its scientific name (“Malus domestica”), but it’s still worth mentioning to solidify the entity connection
    Derived terms: Try not to get too hungry when mentioning “applesauce,” “apple pie,” “apple fritters,” and other apple-y treats—all of which you can get in the “Big Apple”
    Generalizations: An apple is a “fruit” and more generally a “food.” It can also be a color.
    Enumerations: Different types of apples include “Pink Lady,” “Cortland,” and “Honeycrisp,” along with about 7,500 other varieties
    Related concepts: “Apples” are cultivated in an “orchard,” harvested by “picking,” and pressed to produce “cider”
    Co-occurring phrases: Somewhere between derived terms and related concepts, co-occurring phrases are terms often used near or in conjunction with the main topic. Examples might include “bushel” or “bag,” as in “a bushel of apples” or “apples placed into bags.”

A good way to think about semantic keyword research is to find alternative ways of saying the same thing (without being too repetitive) to reinforce the concepts rather than the specific words used to express those concepts.
Structured data layering

Structured data layering is an advanced semantic SEO strategy that combines schema markup for multiple features on a webpage.

The advantage to doing this is to enhance indexing and SERP appearance by building a stronger connection between the various entities, attributes, and values on the page.

Imagine a product page about a new iPhone model. It might include several types of information on the page:

    Product data
    How-to instructions on phone setup or feature usage
    Customer ratings and reviews
    Frequently asked questions about the product and its features and benefits
    Breadcrumbs indicating where the product sits in the hierarchy of models and product types

In the past, some of these different types of content might have been included on different pages in an attempt to capture specific keywords like “iphone howto” or “iphone faqs.” Individual schemas relating to these topics would be included only on pages that contain the corresponding content. 

With semantic SEO, the goal is to build stronger connections between entities. Including all of these things on a single page reinforces the relationships.

It also allows for a more efficient way of including schema markup on the page through the use of the @graph property. Basically, @graph lets you include multiple schema entities in a single snippet of code.

Here’s a schema snippet that includes HowTo and FAQPage scheme for the iPhone example. Ellipses (…) indicate where additional schema would go.
Product Schema

If combining schema in this way seems daunting, you have options. You can still include multiple standalone snippets of schema markup on the page to make the same connections.
User intent mapping

The impact of search intent remains important in the entity-based world of semantic SEO.

When building out your content plan, consider how the nuances between different types of intent can provide different understandings about the relationships between entities.

    Navigational: Users with navigational intent often have a strong idea of where they’re trying to go. Make sure the pages that appear for these searches provide the right elements to guide users to their desired destination.
    Informational: The answers people seek in informational intent searches are often closely related to specific entities—especially searches centered around questions starting with or implying “who,” “what,” “where,” and “why.” (Remember, concepts and ideas are also entities.)
    Commercial: Research-heavy commercial intents tend toward reviews of an entity (such as a product), comparisons of two or more similar entities, or lists of multiple “best,” “top,” or “alternative” entities in the same category.
    Transactional: When users are ready to buy, they want to know everything about the thing they’re buying. Including all of the attribute-value information you can is generally a good idea.

Reframing how you think of search intent in these ways can help you better target entity-oriented search results, and position your content better to appear in AI Overviews.
AI and semantic SEO

Semantic SEO doesn’t just improve your ability to be indexed and understood by Google. Websites optimized for semantic understanding will likely fare better with LLMs and generative AI engines, too.

Generative AI has already undergone significant improvements. That said, Google has about a 25-year head start over its LLM competitors. It’s likely that everyone will stumble over more new problems as progress continues.

With that in mind, here are some ways you can adapt semantic SEO principles to GEO.
How AI models interpret entity-based content

In general, AI models—in particular LLMs—use a similar process to understand entity relationships.

That’s because most current LLMs use the same underlying architecture, Transformer.

This means all contemporary LLMs use the same NLP principles. 

That’s not to say all LLMs are exactly the same. In fact, they can have several significant differences:

    They’re trained on different data.
    They may use different parameters and options at different stages of training and generation.
    Some LLMs may be attuned for specific purposes, such as image recognition and generation or reading and producing computer code.

See NLP advancements: RankBrain, neural matching, BERT, MUM, and embeddings above for more on how NLP processes work.
Using LLMs for strategy and execution

Understanding and organizing entities is literally what LLMs are best at. 

Why not use that to your advantage?

Here are some of the ways you can use LLMs to improve your semantic SEO strategy:

    Extract entities: Feed in existing marketing, product, and related content and have it return the key entities (and related entities) for your business
    Identify topics: Based on the entities you extract, have the LLM identify potential topic areas to write about
    Organize clusters: While you’re identifying content topics, ask the LLM to organize them into pillars and clusters
    Find synonyms: Let the LLM tell you what other words and phrases are synonymous, derived, or otherwise related to the entities in your content
    Generate schema: You weren’t planning on typing out all those schema attribute-value pairs by hand, were you? Didn’t think so.

The boundaries of how LLMs can help with semantic SEO strategy are just starting to be pushed. Once you start getting into the swing of things, you’ll find even more and better ways to use AI to improve GEO signals.
Predicting visibility in AI Overviews with semantic depth

Once you have some content prepared, you may be able to predict the likelihood of your content appearing in AI Overviews and adapt your content accordingly.

To do so:

    Gather the text from AI Overviews for relevant entity and keyword searches
    Likewise, gather the sources cited by those AI Overviews
    Use an LLM to analyze the AI Overviews and sources, then compare them to your content

You may want to try this out for a niche topic or small set of keywords first. 

If it goes well, you can broaden your approach to capture larger topics with deeper sources.
AI tool for semantic gap analysis

You should conduct a semantic gap analysis as part of an overall content gap analysis.

Basically, with a semantic gap analysis, you’re going to look for:

    Missing entities and synonyms
    Missing connections between entities
    Missing attributes that provide a better understanding of existing entities

One tool you can use to help with identifying semantic gaps is the Semrush AI Visibility tool.

From the left navigation bar choose “AI SEO > Visibility Overview.” Then, enter your domain name into the text field and click the “Analyze” button.
Semrush Brand Performance Scaled

Once the analysis is finished running, you’ll see a series of reports with information about your website’s visibility in various AI tools.

You can change which LLM tool’s results you’re viewing by clicking on the drop-down box beneath the row of competitors and choosing your desired tool.
Brand Performance Ai Modes Scaled

To look at possible semantic gaps, scroll down to the “Breakdown by Question” report. 

This paginated report will provide a list of AI Overview questions that you and your competitors rank for. 
Semrush Ai Seo Narrative Drivers Question Scaled

Questions where your competitors rank higher than you may indicate semantic gaps in your content.

You can view the answer from the AI prompt by clicking on the arrow at the far left. This may give you additional insight into where the semantic gaps lie.
Semrush Ai Seo Narrative Drivers Breakdown Scaled
Future of semantic SEO

Semantic SEO has been around a long time, and each year it becomes more relevant. There’s no reason to expect that trend won’t continue.

Nonetheless, here are a few things we can expect:

    The continued rise in prominence of entity-first indexing and entity-oriented search
    The ongoing need for semantically rich, factually correct content to feed generative-AI results
    Multimodal search with semantic relevance to connect between images, videos, and audio—as well as text, of course
    Integration with better privacy tools and improved first-party data sources

If you’re ready to take that step into the SEO future, you can start by learning more