Semantic SEO: How to Optimize for Meaning, Not Keywords

Semantic SEO helps search engines understand context. Learn how to use entities, topics, and intent to create richer, higher-ranking content.

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

While semantic SEO is hardly new, it is now helping to bridge a critical gap between traditional SEO and newer efforts in generative search engine optimization (GEO) and artificial intelligence-assisted optimization (AIO).

Learn why semantic SEO matters, see how search engines and AI tools are using semantic understanding to improve search results, and how to optimize your content in this object-driven environment.

Why Semantic SEO Matters Now

Semantic SEO is more important than ever, as it helps ensure that your content appears in relevant contexts: traditional Google Search Engine Results Pages (SERPs) and newer generative search engine results, such as Google AI Reviews and ChatGPT query answers.

Simply targeting keywords is no longer enough. Google has long since begun to shift from using keywords to using topics and entities to drive search results.

The relationships between keywords, topics, and entities are the semantic relevance that useful, user-oriented content provides.

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

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

For a more detailed explanation of this process and its impact, see the section "How Search Engines Use Semantic Understanding" below.

What is Semantic SEO?

Semantic SEO is the process of optimizing content to take into account meaning, context, and relationships between entities.

The word "semantic" is related to the meaning of words. So, “semantic SEO” is search engine optimization that directly addresses the definitions and intent of content on a website.

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

In an effort to combat keyword stuffing, Google sought to better understand the underlying theme of a page. To do this, it began to rely on other ranking signals to determine that theme.

Relying on topical relevance was better than simply matching keywords. But it still excluded content from results that might have been relevant to the search simply because the right words and phrases were not used.

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

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

• People
• Places
• Subjects
• Concepts and Ideas
• Data and Facts

By understanding the relationships between these entities, Google can provide results that better match a user's search query, even if the search terms don't exactly match the keywords in the content.

Semantic SEO Example: Apple or apple?

Imagine you search Google for "apple."

Google will have to do some work to figure out what you mean:

• Apple, Inc., which makes popular products like the MacBook, iPhone, and EarPods
• An apple, which comes in many varieties, such as Cortland, Gala, and Red Delicious
• Another technical or derived reference, such as a color, botanical term, or phrase like "Adam's apple."

With a little thought, Google will understand that you are probably looking for this particular company and will give you results similar to the ones below.

However, if you search in the plural ("apples"), it will likely return results that focus on the fruit.

If Google based its search results solely on keyword usage, it would likely return ambiguous 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 will see results similar to the ones below, which include results for both the company and the fruit.

Today, Google can mostly avoid this ambiguity by understanding what the user wants to know about. more.

That's why it's important from a content marketing perspective to clearly define your objects and ensure coverage that goes beyond the one or two main topics you want to focus on.

This is where the power of semantic SEO comes in.

How search engines use semantic understanding

Search engines like Google use semantic understanding to discover how objects relate to each other in web content. They then provide users with semantically relevant search results.

Google’s process has three main phases:

1. Google indexes the content of a website by identifying relationships between objects from the structured data of the content and storing them in a knowledge graph.
2. When a user submits a search query, Google analyzes it using NLP tools to understand the user’s intent behind the search.
3. Google then provides a packaged set of search results with various features such as AI insights, a knowledge panel, and search result lists.

There’s a lot going on during each of these phases, so let’s take a closer look at them.

Google’s Knowledge Graph and Entity Indexing

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

To understand what this means, it’s helpful 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 operators to describe the relationships between entities. Graphs have many different uses, such as describing different nodes in a network.

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

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

• Subject: Apple
• Predicate: is
• Object: company

The semantic meaning of this relationship is the predicate "is", which describes "Apple" as a type or instance of the more general entity "company".

Knowledge graphs can be much more complex, but they are all built from three basic structural elements: subject, predicate, and object.

Google's Knowledge Graph works in essentially the same way.

Google collects information about relationships between entities from several sources, including:

• Public sources, such as certain government publications
• Open or public data such as Wikipedia and Wikidata
• Privately licensed data such as sports scores and stock market quotes
• Websites crawled and indexed by Googlebot

It processes and stores data about the relationships between different objects in its Knowledge Graph. It then provides the information in search results in AI insights, featured snippets, knowledge panels, and other SERP features.

For websites, Google recommends using structured markup to describe the relationships between objects. (See the “Schema Markup” section below for more information on how to do this.)

However, Google doesn’t just look at structured data on websites. It also uses NLP algorithms to understand and extract information about objects.

Advances in NLP: RankBrain, Neural Mapping, BERT, MUM, and Embedding

The prevalence of NLP in Google's search algorithm allows it to understand the meaning and usage of words and phrases far beyond simply detecting keywords.

Google has been using NLP in various ways since around the mid-2000s (and possibly earlier):

• 2006: Google Translate launched. Google often ignores this in discussions of AI in search, but offers machine translation of search results powered by its own translation engine.
• 2015: RankBrain is introduced as a method for better interpreting search queries.
• 2018: Neural mapping allows understanding search queries without using exact keywords.
• 2019: Previously launched as an open-source NLP model, Bidirectional Representations of Encoders from Transformers (BERT) is applied to Google Search.
• 2021: Unified Multitasking (MUM) improves BERT by 1000x for understanding and generating language.
• 2023: Reviews of AI powered by the Gemini Large Language Model (LLM) begin to appear as part of Google's generative search experience (SGE).

Each of these steps in the development of NLP has expanded Google’s capabilities into new areas, targeting different stages of the search process.

At the heart of these AI advances is a machine learning concept known as “embedding.”

Without getting too technical:

• Embedding is the representation of categorical data using vectors.
• Vectors are arrays of encoded numbers that list the individual items (“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-time encoding.

Embeddings are used to calculate the proximity of different features to each other for an arbitrary number of vectors, which can be high.

When it comes to search, embeddings power many NLP applications. Here are some of them:

• 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, embedding helps Google understand:

• Apples are a food, or more specifically, a fruit.
• Semantic connections between the general concept of apple and specific varieties, such as Granny Smith, Golden Delicious, or Jonagold.
• More distant connections between physical apples and metaphorical concepts, such as "the apple of my eye."

With the advent of LLM-level learning methods like Gemini, these machine learning concepts and NLP models are taken a step further to provide even more useful results.

AI Reviews and Entity Memory in Generative Results

Google’s expansion of AI Reviews and AI Mode has greatly increased the value of semantic SEO, especially when it comes to brand visibility. This is largely related to the concept of entity memorization.

Entity memorization refers to the ability of LLMs such as Gemini, Claude, and ChatGPT to reference a specific entity in their generated response.

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

• Correctness: Only entities relevant to the question or prompt should be included.
• Completeness: Relevant entities that match the search or prompt criteria should not be excluded.
• Consistency: Asking the same question or prompt should elicit the same entities.

In fact, AI models often have additional parameters built into them, such as limiting the number of entities referenced in the response or favoring newer training data over older ones.

This can cause additional problems with entity rendering:

• Hallucinations: Misunderstanding the semantic relationships between entities and misinterpreting queries or prompts can lead to incorrect or downright strange answers.
• Incompleteness: Knowing what data to include or exclude becomes an exponentially more difficult process as the amount of data used to build an LLM increases.
• Obsolescence: Updating training data can be a difficult task that becomes more difficult as the amount of data collected increases. This may improve as data update processes develop. But it can also lead to other issues, such as knowing when to provide current or historical information.
• Misinformation: The increasing amount of spam targeting LLMs can lead to inaccuracies, undermining the progress Google has made over the decades to reduce spam content in search results.

In order for your brand to appear in AI reviews, it’s important to consider the full implications of these entity recall issues.

The best way to do this is to provide Google and other legal professionals (LLMs) with as much information as possible about your brand as an entity and its relationships to other entities.

This can be done through semantic SEO.

Why entity-based indexing changes search results

By understanding entities, not just keywords, Google can create a much richer set of search results. It has been doing this for years.

Essence-based indexing is a concept first introduced by Cindy Croom as a way to rethink mobile-first indexing.

Essentially, essence-based indexing enhances 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.
• Thus, mobile-first indexing is a misnomer, as it actually refers to the way Google crawls web pages, not how it indexes them.
• The indexing step focuses on understanding the entities (people, objects, concepts, etc.) contained within a page and their relationships to other entities.

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

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

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

Here are some of the most notable entity-based SERP features:

• AI Overview: These increasingly in-depth AI-generated explanations are generated from entity data. (See the “AI Insights and Entity Learning” section above for more information on this topic.)

• Knowledge Panels: These provide a wealth of quick-access information based on relationships between entities, pulled directly from the Google Knowledge Graph. They often highlight a specific relationship between entities, such as the date a company was founded or the nutritional information of a fruit.

• What you need to know: This new SERP feature provides quick facts and information about the subject of a search, often in the form of AI insights or featured snippets.

• People Also Ask (PAA): Questions that appear in PAA sections offer deeper insights into specific aspects of the original search. They typically include AI reviews or selected snippets with entity data from the Knowledge Graph.

• People Also Search (PASF): These SERP features encourage users to search for related or similar items, such as companies in the same industry, foods with similar nutritional value, or people known for similar achievements, such as politicians or actors.

• Top News: Latest news results, reviews, blog posts, and other topical articles require Google to identify the items 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 News, the Latest From feature includes official announcements from a company, such as press releases, official news, blog posts, and other relevant articles. blogs, social media posts, and videos published by a business. All of these are identified by linking the original search item to its assets owned and operated by the business.

• Popular Products: This transaction-oriented SERP feature is based not on the Knowledge Graph but on the Google Shopping Graph, which includes pairwise link data from Google Merchant Center.

• Places: These provide local results even if the query is not a “near me” search. If the business is a business, the locations listed might be retail stores (like Apple stores), while searches related to other businesses might include places where the product can be purchased (like grocery stores or apple orchards).

The above is just a small list of the different types of entity-based SERP features. Google is constantly testing and tweaking features, and new features will continue to emerge as search improves its understanding of the relationships between entities.

Essential Elements of Semantic SEO

There are four main areas to consider when it comes to semantic SEO:

1. Entities, Attributes, and Values
2. Topic Authority
3. Contextual Relevance
4. Schema Markup

All of these are based 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. (For examples of the latter, see the Google Knowledge Graph and Entity Indexing sections.)

The three parts of the EAV model:

• Entity: a person, place, thing, concept, etc.
• Attribute: an associated property or type of entity.
• Value: a specific name for a property or entity.

An entity will often have multiple attribute-value pairs.

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

Apple:

• Variety: Granny Smith
• Color: Green
• Condition: Ripe

Additionally, attributes can themselves be entities.

Cart:

• Apple:
• Variety: Granny Smith
• Color: Green
• Status: Ripe

Apple:

• Variety: Red Delicious
• Color: Red
• Status: Overripe

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

The disadvantage is that this kind of thinking about entity relationships takes practice. This may seem less natural to some people than the subject-predicate-object method.

But if you can shift your thinking to the EAV method, you’ll be in good shape when it comes time to implement schema markup.

2. Topic Authority

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

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

The good news is that topic authority is something you can build over time. But to do it right, you need to be deliberate. Creating high-quality content isn’t enough; you need to be thoughtful in how you structure your topic clusters and content pillars.

Here’s a general overview of how to start building authority in a topic area:

• Develop a forward-looking content strategy that focuses on topics in which you are already an expert and have experience.
• Make sure the topics are relevant to your brand, products, and services.
• Structure your content using the pillars and clusters model. (See the “Content Clustering” section below for more on this technique.)
• Match content to user queries and intent to cover every stage of the customer journey.
• Create evergreen content that will stand the test of time.
• Remove or update content that doesn’t meet performance standards.

Keep in mind that authority also relates 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 reviews, awards, certifications, and other recognition.

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

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

Again, the goal of topic authority is to strengthen the connections between your brand and relevant topics. This can take time. But when you put in the effort, the results are worth it.

3. Contextual Relevance

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

Contextual relevance differs from topic authority in key ways:

• Topic authority looks broadly to ensure that you create content around a variety of products, services, user bases, and other things related to your brand.
• Contextual relevance focuses on a specific page of content to ensure that it includes all entities related to the topic of that page.

One way that NLP models understand context is through embedding (see “NLP Achievements” for a description of embedding).

Contextual embedding specifically helps AI distinguish between different definitions, connotations, ideas, and of course, contexts.

In other words, contextual embedding allows Google to understand when your content is about “Apple” as a company, rather than “apple” as a fruit. (Or vice versa.)

It does this by analyzing other objects that are nearby:

• If it’s surrounded by words like “brand,” “technology,” “stock price,” and “iPhone,” then “Apple” is likely to be about the company.
• If it’s surrounded by words like “tree,” “food,” “skin,” and “pie,” then “apple” is likely to be about the fruit.

Note: These examples use capitalization for readability. However, capitalization is not 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 that the entities being referenced are related to each other in the way you want them to be. When writing and editing your content, consider the following:

• Place brands, trademarks, product names, and similar core business terms next to the features, benefits, user problems they solve, or ideas they most accurately represent.
• Remove words and phrases that do not add relevant value to the entities on the page.
• Vary your phrasing and word choice to show entities in different contexts and connotations. An example would be calling Apple (a company) a "business," a "company," a "tech giant," and an "industry leader" in different places, each of which associates a similar concept with slightly different connotations.
• Identify industry terminology and explain how it applies to your own business propositions to connect your brand to the broader use of these terms.
• Use semantic keywords judiciously to provide the right clues to the search intent on the page. (See semantic keyword research below to learn how to find relevant keywords.)

From a semantic SEO perspective, the ultimate goal is to make sure your content reflects the entity relationships you want to represent.

Of course, always remember that the ultimate goal of any content is to be useful to users.

4. Schema Markup

Schema markup is structured data that reinforces semantic signals. It’s used in the backend code of a web page, so users won’t see it.

But Googlebot and other search robots definitely see it.

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

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

JSON-LD uses JavaScript code to create attribute-value pairs for entities. It is fairly easy to read and understand for beginners, although it may take some time to learn all the different types of entities and values ​​that exist.

For example, consider this schema fragment from the Apple.com homepage:

Within the tags and the braces ({}), the paired data looks like this:

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

Google supports a large list of entities that can be included in schema markup. Many of these describe information that Google can upload to the Knowledge Graph and include in SERP features.

For example:

• Navigation chains can appear in search results below the page title.
• Review information can appear for products, services, organizations, media, and more, including ratings and snippets.
• Event details can appear when searching for artists, venues, classes, or entertainment.
• Job listings can provide job seekers with information about career opportunities.

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

Strategies for Implementing Semantic SEO

Implementing semantic SEO involves looking at your content and its underlying code from an indexing perspective that takes into account the specifics of the entity. Each of the methods below provides general guidelines to consider.

Entity Optimization

Optimize entity relationships by researching search results similar to the ones you want to appear in.

The first step is to do some research:

• Learn how to track the presence of entities in the knowledge graph
• Identify the entities you want to associate your website and brand with
• Browse knowledge panels for search queries similar to the ones you want to rank for, and pay attention to the attribute-value pairs that appear.
• For the same search queries, look at what other SERP features are showing up and see how they align with Google’s structured data guidelines.

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

Don’t limit yourself only high-level entities such as Organization, Product, Person, and FAQ. Include as much information as possible that makes sense for each type of content you publish.

For example:

• If you allow customer ratings and reviews, use the Reviews Snippet schema.
• If you have a jobs page and a list of open positions, use the Job Listing schema.
• If you offer training and certification courses, use the Course Listing schema.

Review all of Google's supported structured data markup to see which snippets are relevant to the types of content you publish.

Content Clustering

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

Types of related topics around which you can create content clusters include include:

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

When developing your content strategy, remember that pillars are not isolated elements.

In fact, it’s entirely expected that your content creation will generate pieces of content that span multiple pillars.

Think about how the entities underlying each broad topic relate to the other entities you’re discussing. You can approach this in a few ways:

• Site Structure: How does the physical layout and URL structure of your site signal connections between different topics?
• Navigation: How can you reinforce connections between entities through menus, breadcrumbs, footer links, and other navigation tools?
• Internal Linking: Are you using correct and consistent anchor text for links between pages within clusters and between topics?

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

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

Semantic Keyword Research

Semantic keyword research involves studying the meanings of the words people are searching for, not just the general intent of the search.

When looking for related keywords to target, broaden your efforts to include terms that can be covered by the same topic.

Semantic considerations for keywords for "apple" (fruit) might look like this:

• Synonyms: The word "apple" may not have many synonyms, other than its scientific name ("Malus domestica"), but it's still worth mentioning to solidify the connection between the entity.
• Derived terms: Try not to get too hungry when you mention "applesauce," "apple pie," "apple fritters," and other apple delicacies—all of which can be found in the Big Apple.
• Generalization: An apple is a "fruit" and, in general, a "food." It can also be a color.
• Lists: Different types of apples include "Pink Lady", "Cortland", and "Honeycrisp", as well as about 7,500 other varieties.
• Related concepts: "Apples" are grown in an "orchard", harvested by "harvesting", and pressed to produce "cider".
• Related phrases: Somewhere between derived terms and related concepts, related phrases are terms that are often used alongside or in conjunction with the main topic. Examples would be the words "bushel" or "bag", such as "bushel of apples" or "apples placed in bags".

A good way to approach semantic keyword research is to find alternative ways to say the same thing (without excessive repetition) to reinforce 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 web page.

The benefit of this is that it improves indexing and appearance in SERPs by creating a stronger connection between the different entities, attributes, and values ​​on the page.

Imagine a product page about a new iPhone model. It can contain several types of information on a page:

• Product details
• Instructions for setting up your phone or using features
• Customer ratings and reviews
• Frequently asked questions about the product, its features, and benefits
• Navigation tables that indicate the product's place in the hierarchy of product models and types

In the past, some of these different types of content might have been included on different pages to try to capture specific keywords, such as "iPhone instructions" or "iPhone FAQs." Separate schemas related to these topics were only included on pages that contained relevant content.

The goal of semantic SEO is to create stronger connections between entities. Including all of these things on one page strengthens the connections.

It also allows you to more efficiently include schema markup on a page using the @graph property. Essentially, @graph allows you to include multiple schema entities in a single code snippet.

Here’s a schema snippet that contains the HowTo and FAQPage schemas for the iPhone example. The ellipsis (…) indicates where to insert additional schemas.

If combining schemas like this seems complicated, you have options. You can still include multiple separate schema markup snippets on a page to create the same connections.

Mapping User Intent

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

When developing your content plan, consider how the nuances between different types of intent can provide different insights into the relationships between entities.

• Navigational: Users with navigational intent often have a clear idea of ​​where they are trying to get to. Make sure the pages returned for these search queries contain 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 search queries that begin with or imply “who,” “what,” “where,” and “why.” (Remember that concepts and ideas are also entities.)
• Commercial: Commercial intents that focus on research tend to be reviews of an entity (e.g., a product), comparisons of two or more similar entities, or lists of several "best," "best of," or "alternative" entities in the same category.
• Transactional: When users are ready to buy, they want to know everything about the thing they are buying. Including all possible attribute and value information is usually a good idea.

Rethinking your search intent in this way can help you better target entity-based search results and better position your content for display in AI reviews.

Artificial Intelligence and Semantic SEO

Semantic SEO doesn’t just improve your ability to be indexed and understood by Google. Websites optimized for semantic understanding are also likely to perform better with LLM and generative AI engines.

Generative AI has already seen significant improvements. Still, Google has about a 25-year lead over its competitors in LLM. It is likely that everyone will encounter new challenges as progress continues.

With that in mind, here are some ways to adapt semantic SEO principles to geographic location.

How AI Models Interpret Entity-Based Content

In general, AI models, especially LLMs, use a similar process to understand the relationships between entities.

This is because most modern LLMs use the same underlying architecture, Transformer.

This means that all modern LLMs use the same NLP principles.

This does not mean that all LLMs are exactly the same. In fact, they can have several significant differences:

• They train on different data.
• They can use different parameters and options at different stages of training and generation.
• Some LLMs can be customized for specific purposes, such as image recognition and generation, or reading and creating computer code.

See the article “Advances in NLP: RankBrain, Neural Mapping, BERT, MUM, and Embedding” above to learn more about how NLP processes work.

Using LLMs for Strategy and Execution

Understanding and organizing entities is literally what LLMs do best.

Why not use this to your advantage?

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

• Entity Extraction: Enter your existing marketing, product, and related content and let it return key entities (and related entities) for your business.
• Identify Topics: Based on the extracted entities, ask LLM to identify potential topic areas for writing.
• Cluster Organization: As you identify content topics, ask LLM to organize them into pillars and clusters.
• Find Synonyms: Let LLM tell you what other words and phrases are synonyms, derivatives, or otherwise related to the entities in your content.
• Create Schema: You didn’t plan on manually entering all those schema attribute-value pairs, did you? I didn’t think so.

The boundaries of how LLM can help with semantic SEO strategy are just beginning to expand. Once you start getting into the process, you’ll find even more and better ways to use AI to improve geo-signals.

Predicting Visibility in AI Reviews with Semantic Depth

Once you’ve prepared your content, you can predict its likelihood of appearing in AI reviews and tailor it accordingly.

To do this:

• Scrape the text from AI reviews to find relevant entities and keywords.
• Similarly, gather the sources that these AI reviews cite.
• Use LLM to analyze AI reviews and sources, then compare them to your content.

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

If all goes well, you can expand your approach to cover broader topics with deeper sources.

An AI tool for semantic gap analysis

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

Essentially, with semantic gap analysis, you will look for:

• Missing entities and synonyms
• Missing relationships between entities
• Missing attributes that provide better understanding of existing entities

One tool you can use to identify semantic gaps is the Semrush AI Visibility tool.

In the left navigation bar, select “AI SEO > Visibility Overview.” Then, enter your domain name in the text box and click the “Analyze” button.

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

You can change the LLM tool results you’re viewing by clicking the drop-down menu below the competitors row and selecting the tool you want.

To view possible semantic gaps, scroll down to the “Question Breakdown” report.

This paginated report will provide a list of the AI ​​review questions that you and your competitors rank on.

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

You can view the answer in the AI ​​query by clicking the arrow at the far right left corner. This can give you some additional insight into where semantic gaps lie.

The Future of Semantic SEO

Semantic SEO has been around for a long time, and it’s becoming more relevant every year. There’s no reason to expect this trend to stop.

However, here are a few things we can expect:

• The continued rise in popularity of entity-based indexing and entity-based search.
• The continued need for semantically rich, factually correct content for generative AI.
• Multimodal search with semantic relevance for linking between images, video, and audio, as well as, of course, text.
• Integration with better privacy tools and improved first-party data sources.

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