The Advanced Skill Most SEOs Pretend to Understand

Semantic SEO sounds complicated, but it simply boils down to doing SEO without cutting corners. 

If you do SEO properly, you’re automatically doing semantic SEO. It’s just that most people aren’t doing it properly… 

It’s not a different type of SEO. You don’t need to do wildly different things. Rather, it’s a mental model that advances:

• The way you think about SEO strategy
• The SEO goals you aim for
• The processes you follow to achieve them

This is a no-hype, no-bs guide on how you can implement semantic SEO on your website.

We’ll cover what “semantic” means, how it applies to search engines and LLMs, and how I and the following experts actually do semantic SEO and get meaningful results for clients.

Let’s dig in.

What does “semantic” mean? 

The word semantic means “of or relating to meaning”.

For example, the word “dog” has meaning to us, “asdf” does not, it’s just a random string of characters.

To machines, all words are random strings of characters. The field of semantics focuses on training them to interpret the meaning of words based on how we (humans) use them.

Search engines don’t speak English. They speak code. Semantic SEO is about translating your meaning into their language. 

The more popular a particular sequence of characters is, the higher the chance it has meaning.

The more two separate strings are used together, the more likely they are related.

Notice the language I’m using — “more likely”, “higher the chance” — it’s all a matter of probabilities and calculations because machines cannot truly understand things the way we do.

Repetition and patterns in how humans use words are how they infer meaning.

That is the basis of semantic search.

So then, what’s semantic SEO about? 

Semantic SEO is about showing up in search engines and LLMs that surface content or create responses based on meaning rather than word strings.

They typically work by matching the topics in a user’s query with documents that cover that topic well.

This is different from old-school search engines that match content based on the exact words used (a bit like how Google Scholar works today).

The way all senior SEOs I interviewed think about it is as an overlap between:

• Brand: To ensure machines understand and represent your brand accurately.
• Content: To connect your brand to core topics you want to be a trusted source for.
• Technical: To ensure your brand, content, and website are machine-friendly.

It’s where brand strategy overlaps with technical and on-page SEO — and that overlap is growing. 

It’s all focused on how machines interpret your brand and content so they can mention you in more responses, accurately.

The goals of semantic SEO

Rankings and traffic have long been the staple goals of traditional SEO projects. However, they’re concerned with if a brand shows up in search results.

It doesn’t necessarily matter how because the expectation has been that content will be featured verbatim as it is on the brand’s website. Sure, Google uses different styling to emphasize relevant parts to searchers, but it doesn’t completely rewrite your content.

For instance, this search result displays the post’s first sentence word-for-word:

The goals of semantic SEO, however, are much more concerned with how a brand is featured.

• Is the brand accurately described and represented?
• Is it showing up as an authoritative, trusted source for the correct topics?
• Is the sentiment surrounding the brand mention positive?
• Is the brand’s thought leadership being recognized and cited?

These are the questions that now matter but traditionally were not a concern.

This is because of how modern search engines and LLMs present answers. Thanks to AI features, they can now rewrite a brand’s content in confident, authoritative-sounding prose. They can (and often are) confidently wrong in a way traditional search results could not be.

They also tend not to use your brand’s content verbatim.

Rather, they summarize your content based on their understanding and interpretation (a lot of which is formed from what other people say about your brand or topic).

So, to do SEO properly these days, you have to understand how search engines have adapted over the years and what factors now influence your brand’s visibility.

How semantics apply to search (and why Google’s not truly a semantic search engine) 

Search engines (and now LLMs) can retrieve information and present it to searchers in different ways.

• Lexical search is based on matching word strings, like when you search for an exact song lyric. It also treats words like “bat” and “bar” as similar because they start with the same sequence of characters.
• Semantic search is based on predicting patterns and inferring the meaning of words and their relationships. Most LLMs use this approach which is why they can better connect “hypoallergenic dogs” to “low shedding dogs” despite these terms not having much lexical similarity.
• Hybrid search blends the two together, which is what most search engines use today, including Google, Baidu, and others. It allows the best of both types of searches by running on a lexical base with some semantics overlaid on top.

Elie Berreby explains this very well:

The way in which semantic processes are used for information retrieval affects how your content and brand gets surfaced.

For example, Baidu has created both a lexical index and a semantic one, allowing it to index content in both ways. Google, has used vectorization for a long time and heavily relies on semantic processes during the reranking stage, right before choosing which results it thinks will be best for a searcher to see.

On the other hand, LLMs are almost completely semantic and rarely use lexical or hybrid methods.

In a nutshell, LLMs tend to be purely semantic, while modern search engines use a lexical base that’s semantically augmented in different ways.

Will search engines, like Google, become purely semantic?

According to Olaf Behrendt (Senior Data Scientist at Yep) and Brandon Li (Machine Learning Engineer at Ahrefs), it’s unlikely Google or other search engines will become fully semantic and completely replace lexical search for a few reasons:

1. It’s cost and resource prohibitive.
2. Exact match (lexical) search is still a dominant way people use Google.
3. Fully semantic results are currently unreliable and untrustworthy.

Things may definitely change in the future, especially with new features like Google’s AI mode becoming more commonplace. However, until then, keyword-level optimization will remain an important baseline for showing up in traditional search results.

Entity SEO (and other semantic SEO processes) will need to enhance your baseline keyword strategy to increase visibility in LLMs or AI-driven areas of search results, such as AI Overviews.

How experts actually “do” semantic SEO 

So, all this theory is good to know, but you might be wondering what to do with it. Remember, doing semantic SEO doesn’t require anything different than regular SEO.

It’s just a more advanced way of thinking and focuses on optimizing for meaning. It’s about caring how your brand and content show up, not just if they do.

This is why semantic SEO was cited as one of the top advanced SEO skills in a recent poll among 100+ SEO experts. So, let’s look at how experts apply semantic thinking to common SEO processes.

1. Define your brand and build a universal brand guide

Creating a brand guide ensures your brand is consistent everywhere it’s featured. It also aligns everyone in your company to refer to it the same way in all communications.

Ensuring a brand is clearly defined and communicated is one of the biggest focus points of semantic SEO since machines cannot infer meaning from your brand name alone:

• Apple — could connect to the fruit
• Nike — could connect to the Greek goddess of victory
• Adidas — has no semantic meaning outside of its brand

Specifically, it’s all about the technical side of branding and codifying your brand guide so machines interpret who you are and what you’re about correctly.

Brand has to be a distributed source of effort because when you have thousands of employees, you can’t control every touchpoint. You need to codify it to keep it consistent. 

Perhaps more importantly, codifying your brand allows you to also explain to others the correct way to refer to you. Think of media kits, public logo files, and correct and incorrect ways to shorten your brand name.

Sidenote.

Codifying in this context does not mean to turn your brand into code. Rather, it’s about creating a well thought out plan or system about how your brand should be represented and documenting it in clear brand guidelines for internal (company) and external (media) use. 

For example, here’s Ahrefs’ media kit, where we make it easy for others to reference our brand the same way we do.

Since LLMs learn a lot about your brand from what others say, the more consistency there is between how you self-reference your brand and how others talk about you, the more likely LLMs will interpret and surface the correct information about you.

You need the internet to talk about you in a consistent way. That’s what gives your brand context beyond your own ecosystem.

Otherwise, LLMs may hallucinate responses based on misleading data or other people’s opinions.

2. Connect your brand to features and attributes people care about

Once you clarify who you are and what you do, you’ll need to connect your brand to things LLMs and semantic search engines can use to understand more about you.

Connecting your content to core entities and topics is already fairly standard practice.

However, advanced SEOs also connect the brand to features and attributes of these entities that matter most. Think of it like how:

• Apple connects to innovative technology
• Nike connects to performance footwear
• Hubspot connects to inbound marketing

Remember, when doing semantic SEO, we’re optimizing for meaning. Brand names on their own have no tangible meaning, so we need to create that meaning for semantic search engines to latch onto.

This is more than just adding specific words or entities in your content.

You can’t just say you’re the “best at X” or “the most Y.” It’s about other people saying this about you, too. This ultimately comes down to branding, something that traditional SEO has not concerned itself too much with.

You can get started with Ahrefs’ Brand Radar. Check out either your brand or competitors’ to spot what descriptive words, audience segments, or product categories get mentioned in AI Overviews:

These are the features and attributes that LLMs connect to brands in your industry. Pick the one you care most about because this isn’t a matter of being known for everything. Instead, good branding comes down to being known for how well you do one thing.

For example, I successfully did this for a local aged care facility.

This was prior to AI Overviews being launched, so I used Google’s autosuggest at the time and noticed that attributes related to quality and price were commonly searched:

By connecting their new brand to these attributes, we could position them as the #1 choice for people who prioritize “value for money.”

It’s more than just saying your brand is #1.

You also have to prove it using authoritative, indisputable sources or some other mechanism that builds trust.

So, for this project, my team and I used government data that allowed us to show how this aged care facility:

• Was #1 in their local service area (compared to 238 other local facilities)
• Ranked in the top 1.26% of their entire city for “resident experience”
• Offered 50% more floor space (compared to 450 alternatives from competitors in their city)
• Was up to 33% cheaper on average (compared to 148 competitors)

We integrated this data either as micro-copy or entire sections everywhere it made sense to add it, like the:

• Home + about pages
• Accommodation pages
• Pricing documentation
• Citations + directory listings
• Ad titles and descriptions
• Page titles and descriptions

In my interview with her, Sally also endorsed this approach:

Don’t silo your identity to your About page. The homepage, service pages, even your footer — they all reinforce who you are to a machine. 

Because we used data from an authoritative and immediately trustworthy source, we could be bold in our messaging and say things like:

We’re the #1 facility for resident experience in {city}.

Or…

Our rooms are twice as big and up to 33% cheaper compared to 450 alternatives in {city}.

Anyone else who spoke about the brand and saw the stats based on government data could then trust our data’s source and be more inclined to repeat this messaging.

Thanks to this approach, some LLMs selected this aged care facility as the #1 choice when asked about “value for money”:

Perplexity also went a step further and created a comparison table:

It hallucinated some points about typical facilities in the city… but it got all the remaining stats about this local business correct, most likely due to the consistency, clarity, and frequency with which we communicated them.

This result is a major early win, considering this aged care facility was still a new player in the market, didn’t yet rank organically for related keywords on search engines, and did not use the words “value for money” on their website.

That’s a semantic SEO win right there, something traditional keyword-based SEO would be unable to achieve.

3. Add keywords (and meaning) to “alphabet soup” URLs

Have you ever worked on a project where the URLs were automatically created by a CMS and looked like site.com/kj72376g8js?

That’s what I call “alphabet soup” URLs since they’re just a random string of characters that make no sense to machines or humans.

Converting these to user-friendly and search-engine-friendly URLs improves SEO, but it can certainly be a challenging process. Semantic SEO can help make the process easier, though!

For instance, you can use many tools that provide semantic information about each page on the site, like:

• Top ranking keywords
• Page titles and descriptions
• H1 headings
• Body content, and so on.

To keep things simple, I like to use Ahrefs’ Top Pages report if the site has been around for a while.

In one easy view, you can connect URLs to their best-performing keyword and streamline your approach to changing and redirecting URLs.

Not only that, but for large sites, you also get built-in prioritization since you can arrange the pages in the order of:

• The traffic they’re currently getting: so you can bump up the best-performing pages even more or identify the weakest pages that need some extra attention.
• The number of keywords they rank for: so you can improve on-page optimization of pages with the highest potential for a quick traffic boost.
• The volume of the top keyword: So you can evaluate missed potential due to poor optimization and prioritize pages with the most searches per month.

For newer sites with no performance yet, you can use Ahrefs’ Site Audit instead. Check out the Page Explorer report and customize the columns:

You can use the following highlighted fields in the “Content” section to extract keywords, entities, or other semantically meaningful content to use in your URLs:

You can also take it up a notch and use semantic text analytics software to extract the most dominant topics and entities on each page. Some options worth trying (depending on your technical skill level) include Google’s Natural Language API and Text Razor.

What you’re looking for is a fast way to connect each page to a specific topic it talks about, then turn that topic into the slug to replace the alphabet soup (with 301 redirects, of course).

4. Map out a user and search-friendly information architecture

Most SEOs think of information architecture as “URL structure”, but it actually also involves:

• Navigation + menus
• Internal linking
• Taxonomies (like categories and tags)
• Labels you use for pages and categories
• Filters and faceted navigation systems

Traditionally, mapping out all these elements is part of the UX design process. Where most designers go wrong is that they don’t align these elements with keywords that people search for.

Advanced SEOs work alongside design teams to ensure these elements are all not only keyword optimized but also semantically optimized.

My approach here is to use the EAV model (entity-attribute-value):

	What is it	Example in action
Entity	Represents the object or item you’re optimizing.	Products, categories, users
Attribute	This is a characteristic or feature of the entity	Colors, sizes, materials
Value	This is the specific information tied to the attribute	Red, medium, cotton

This is especially helpful for sites that need to organize collections of listings like:

• E-commerce stores (organizing product listings)
• Marketplaces (organizing marketplace items)
• Real estate (organizing property listings)
• Job boards (organizing job listings)
• Directories (organizing business listings)

The listings are the entities you’re optimizing for.

The collections of listings are generally where you’ll need to consider the features and attributes that apply. The exact values that you use will come from keyword research. These tend to be adjectives or descriptive modifiers used in keywords.

Here’s an example of how I would map out the relevant features and attributes for an ecommerce store selling saws:

Most SEOs create collection pages based on these features. But the best ones also extend that to the taxonomies (categories and tags), filters, and navigation elements. Even microcopy like page and product titles can benefit with these attributes clearly included.

For large sites with lots of listings, you can automate a lot of the tagging and labeling for your listings and their images with tools like Filestack. A lot of its intelligence features are semantic in nature since they interpret meaning (and even emotions) behind images and text.

This is the secret to continual growth even through multiple algorithm updates. Here’s an example of one of my B2B ecommerce clients for whom I created a semantically-optimized information architecture 4+ years ago.

They attribute this approach to semantic SEO as the #1 factor that allowed them to grow organically year-over-year, remaining unaffected from algorithm updates along the way.

5. Add information gain to your content

Adding information gain to content aligns with a semantic approach to SEO, one that prioritizes meaning, relevance, and contribution to a broader knowledge graph.

Content writing is the backbone of most SEO. Yet, traditional thinking (enforced by content optimization tools) is to:

• See what already ranks
• Reverse engineer it’s on-page optimization
• Copy the blueprint and make at least 10% “truly original”

Most of this comes down to cramming keywords and entities into your content.

There are a few things wrong with this approach. Firstly, it’s the biggest reason why most SEO content becomes just another indistinguishable drop in the sea of sameness.

Secondly, it’s basically a slightly more nuanced version of keyword stuffing.

More advanced writers will do more than remix existing content. They will aim to contribute something new to the conversation so their content truly stands out and is helpful to their audience.

That’s why at Ahrefs, we took the approach of surfacing interesting and relevant topics in our AI Content Helper instead of providing a list of terms to try and squeeze into your content.

Here are some helpful guides for leveling up your content further and standing out in the sea of sameness:

6. Close page-level topic gaps with content improvements

One of my favorite use cases of semantic SEO is closing page-level topic gaps when updating content.

Content updates are a stock standard thing people do for SEO these days to maintain freshness. When you also close topic gaps, that’s a semantic task because it’s about covering meaningfully related concepts, not just sprinkling in missing keywords.

But, it’s one thing to say, “add more topics” to content and it’s another to know exactly what topics to add and exactly where and how to do it.

The easiest method is to check out Ahrefs’ AI Content Grader.

You can compare your content alongside the top-ranking posts and get a side-by-side score for how well you each cover specific topics.

You can also get topic improvement recommendations:

Another method I’ve had great success with is checking out the keywords a post used to rank quite well for, especially if it was ranking but didn’t explicitly mention the topic in the content.

You can see this in Site Explorer > Organic Keywords. I like to click and drag the graph to compare the peak traffic with the lowest point in a decline afterward. It shows up as an orange highlight like this:

Then, check out the exact keywords for which you lost visibility. I prefer to order the list to show the keywords with the greatest traffic change up the top:

Usually, a drop in performance can be because:

• Your content may be getting stale if it’s a few years old
• Competitors cover the sub-topics better or more explicitly
• Search intent for your target keywords has changed

No matter the case, you can look for patterns in the topics you lost visibility for and optimize your content better for them.

In the above example, all of the top keywords that lost visibility were about “CGT,” or capital gains tax, specifically in relation to the 6-year rule.

However, the content talked about these terms separately and never optimized them together. For instance, the main heading was “Understanding the 6-year exemption rule on property investment”, no mention of CGT.

None of the CGT sections in the content mentioned the 6-year rule. So that’s one of the major gaps we closed when updating this piece:

This approach made all the difference in performance:

7. Build “topical authority” at a site-wide level

When semantic SEO is mentioned, many people immediately equate that to “topical authority” — the idea that your site should cover a subject deeply and thoroughly so that search engines see you as a trusted source on the topic.

A lot of people translate this as writing about anything and everything related to your brand’s main topic.

This thinking is responsible for a lot of SEO content spam that has flooded the internet in recent years.

It would be the equivalent of thinking a brand like Nike should create content about everything related to shoes — including banal things like:

• What is a shoe?
• History of shoes
• Types of footwear

Don’t do this. It doesn’t work.

It’s also not what semantic SEO is truly about.

What’s missing in this thinking is the topic’s relevance to your brand. Remember the Venn diagram at the start of this post?

Connecting your content to your brand goals is what separates advanced thinking from basic thinking. It allows you to take on more nuanced challenges and help brands identify which keywords are worth targeting over others.

For example, the terms “product design software” and “product design tools” relate to different services and business types. One is about physical product design (like designing tangible products you can manufacture), and the other is about digital product design (like prototyping SaaS apps and websites).

They have very low semantic similarity despite being similar on a lexical (word) level.

You can verify this in Ahrefs’ SERP comparison feature, which shows you how similar results between keywords are and whether you can target them in the same content strategy or not:

In this case, the same website should not target both; otherwise, you’d be confusing semantic search engines and LLMs about what your brand actually does.

Check out my full process for How to Build an SEO Topical Map That’s Relevant to Your Brand if you want to master this skill more deeply.

8. Create clear, structured data with schema and semantic HTML

Structured data is a powerful data source for search engineers.

They can pull from several different sources around the web, but you should carefully optimize two on your website: schema markup and semantic HTML.

“Careful” is the operative word here.

A lot of people use structured data to try and signal things that don’t exist in the real world. That just muddies the data and increases the likelihood you’re ignored.

This sentiment was echoed by Brandon, one of Ahrefs’ data scientists with a robust skill set in knowledge graph architecture. He mentioned structured data as a useful data set if it remains clean, well organized and used properly.

Otherwise, it can “mess up [his] data set,” and he’s less inclined to use any data that’s messy or inaccurate when building out a knowledge graph.

So, the more SEOs pollute a data set by incorrectly optimizing it or abusing it, the less effective it becomes as a way to surface content.

Thankfully, it’s really easy to use schema correctly. Schema is like a translator for your content. It gives it structure so machines can better understand what’s on your website.

Adding descriptive schema markup to a website’s web pages provides the missing piece for machines: context. That is, how one entity is related to another. For example, how the business (Organization Type), offers a service (Product/Service Type), for a particular audience in one or more geographies.

Dentsu has a great schema markup generator:

You can use this to:

• Define your brand from a technical perspective by using organization schema
• Disambiguate your brand in cases where it shares a name with another brand or entity
• Optimize core entities like products and people that connect to your brand
• Connect your brand to core topics you want to increase visibility for

On the other hand, semantic HTML is about the code structure of your content. It uses code that makes more sense to both humans and machines.

For example, instead of using a generic