We first wrote about semantic search back in 2020, when it was just starting to gain attention. A lot has happened since then. ChatGPT was launched, AI Overviews showed up in search results, and understanding meaning—not just keywords—became central to how search engines work. Because of all this, it was time to update this article.
Search engines “think” in topics, not keywords. They understand entities—people, places, products, ideas—and how they relate. They focus on meaning, not word matching. If you want to do SEO today, or show up in AI recommendations, you need to understand this shift. It’s not optional. It’s how search works now. Search for “how tall is the guy who played Wolverine.” Google knows you’re asking about Hugh Jackman’s height—even though you never typed his name. It understands “guy who played Wolverine” refers to a specific person and gives you the answer: 6′2″.
Semantic search works in four ways that make it feel like a huge step forward from old-school search.
Semantic search connects related words
Semantic search knows that “cheap,” “affordable,” and “budget-friendly” all mean similar things. It understands “spouse” includes “wife,” “husband,” and “partner.” This is called query expansion—the system automatically broadens your search to include synonyms and related terms. When you search for “cheap flights,” it also looks for content about “affordable flights,” “budget flights,” and “low-cost airfare” without you asking. So, you don’t need to write separate content for each variation. One good article covers them all.Semantic search recognizes things (entities) and how they relate
Search engines now access databases of entities—people, places, products, companies—and understand how they connect. This is stored in knowledge graphs—massive databases that map relationships between millions of real-world things. To populate these graphs, search engines use entity extraction—algorithms that scan content and identify references to specific people, places, organizations, and concepts. When your page mentions “Tim Cook,” entity extraction recognizes this as Apple’s CEO, not a random person named Tim who cooks. Here’s another example: Search for “who’s the partner of the actor who played Obi-Wan.”
- Know Obi-Wan is a character.
- Know multiple actors played him and have some conception of who the most popular one was.
- Understand “partner” means romantic partner.
- Find the right person.
Semantic search figures out what words mean in context
About 40% of English words have multiple meanings. “Apple” could mean the fruit or the tech company. “Jaguar” could be an animal or a car brand. Semantic search uses context—your location, search history, the other words in your query—to figure out which meaning you want.Semantic search understands what you’re really looking for based on the outside context
When the coronavirus became a pandemic in early 2020, Google recognized that people were mainly looking for information about COVID-19. As a result, for searches like “corona,” which can have multiple meanings, Google reordered the results to show information about the virus first, while pushing results about Corona beer and other meanings further down. This change is easy to see when looking at historical data in Ahrefs’ Keywords Explorer.
You don’t need to understand all of the technical details, but knowing these exist helps explain why everything changed.
How search engines organize information
Before understanding meaning, systems break text into pieces through tokenization — splitting sentences into words or subwords that models can process. But that’s just step one. To understand what content is about, search engines need to recognize real-world things and how they relate. This is where knowledge graphs come in—structured databases that store facts about entities (people, places, products, companies) as simple relationships: Entity → Attribute → Value For example, Google’s Knowledge Graph might store:- iPhone 17 Pro → price → $1099
- iPhone 17 Pro → release date → September 2025
- iPhone 17 Pro → camera resolution → 48MP
Vector embeddings
Search engines also convert content into mathematical representations called vector embeddings — coordinates that capture meaning. This lets them find conceptually similar content even when the wording differs completely.
The major technological milestones
Beyond the Knowledge Graph, Google has introduced several advances that deepened semantic understanding:- RankBrain (2015). If you’ve ever heard of “LSI keywords,” forget them. RankBrain, an upgrade to Hummingbird, solves the same problem LSI tried to solve, but better. It understands the meaning of unfamiliar words and phrases using machine learning—crucial since 15% of all search queries are new every day.
- BERT (2019). Improved understanding of how words relate in sentences, especially for complex queries where word order matters.
- MUM (2021). Handles complex, multi-step questions across 75 languages.
- Gemini (2024). Google’s latest AI model that understands text, images, video, and audio together. Powers AI Overviews and AI Mode.
How it all fits together
Modern search works in stages. First, a fast retrieval layer pulls a large pool of potentially relevant pages based on keyword matches and semantic similarity. Then a more sophisticated model re-ranks that shortlist: Does this page answer the query? Does it match the intent? Is the source trustworthy? This is why keyword stuffing fails. Even if your page makes the initial pool, the re-ranking stage evaluates quality in ways that gaming can’t fake.That’s how it works. Here’s what it means for your content strategy.
Topic coverage beats keyword targeting
Because semantic search understands that “python tutorial,” “python guide,” and “learn python” mean the same thing, you can’t rank separate pages for each variation anymore. Google will pick one page to rank for all of them. Our article on SEO forecasting ranks in the top 10 for dozens of keyword variations—not because we optimized for each one, but because we covered the topic thoroughly. That’s the shift: comprehensive content on a topic beats a portfolio of thin pages targeting keyword permutations.
Further reading
Search intent is everything
You can write the most technically perfect article about “SEO report,” but if people searching that term want a template, not an advanced tutorial, you’ll struggle to rank.
Further reading
Brand and authority become ranking factors
Semantic search systems understand who’s talking. When your brand becomes a recognized entity in the Knowledge Graph, your content gets more trust. This effect extends to AI-powered search, which is built on the same semantic foundations. A study of 75,000 brands found that branded web mentions correlated strongly (0.66–0.71) with visibility in ChatGPT, AI Mode, and AI Overviews. Traditional SEO metrics like backlinks and page count showed much weaker correlation.
Now that you know what matters, here’s how to actually do it.
1. Match search intent and cover the topic comprehensively
Before you write a single word, you need to understand two things: what format searchers want and what information they expect. First, check the search intent. The easiest way to understand what searchers want is to analyze the current top-ranking results using the three Cs of search intent:- Content type. Are the top results blog posts, product pages, landing pages, or category pages? If the top 10 positions show blog posts, don’t try to rank a product page.
- Content format. What format dominates the results? How-to guides, step-by-step tutorials, listicles, reviews, or comparisons?
- Content angle. What’s the unique selling point of the competing content? Look for patterns like “free,” “for beginners,” “2025,” “fast,” or “cheap.” These angles tell you what matters most to searchers.
- What subtopics do most of them cover?
- What headings appear consistently across multiple articles?
- What questions do they answer that you haven’t addressed?
- Are there specific examples, data points, or tools they all mention?
- For new content: Enter your target keyword and the tool analyzes the top-ranking pages to show you which subtopics you need to cover. Use that to build your outline.
- For existing content: Paste in your article and the tool spots missing topics, then suggests exactly how to fill those gaps. It gives you a content score out of 100, showing where you stand compared to top-ranking pages.
2. Link your related content together
Internal linking helps connect your content in a meaningful way and shows search engines what you’re knowledgeable about. Google looks at the words you use in links—and the text around them—to understand what the linked page is about. Clear, specific link text makes this much easier. For example, if you link from your keyword research guide to your article on low-competition keywords using clear, descriptive wording, you’re showing search engines that these topics belong together. You’re essentially laying out your expertise and making your site easier to understand. So, think of your site as a set of connected themes (aka topic clusters), not isolated articles. Your broad, in-depth guides (often called pillar pages) should link out to more focused posts. For example, if you have a complete SEO guide, it should naturally link to individual articles on keyword research, link building, and technical SEO. This helps both readers and search engines see how everything fits together.
Recommendation
The same principles apply to backlinks. When other sites link to you using topically relevant anchor text, it helps search engines understand what topics you’re associated with. Something to keep in mind if you’re running a link building campaign.
Further reading
3. Build consistent information about your brand everywhere
Semantic search builds entity profiles, connecting your brand to attributes like founders, locations, products, and claims. AI systems construct these profiles from whatever sources they find: Reddit threads, Medium posts, Quora answers, random blog articles.
- Fill information gaps with specific official content. Create an FAQ that addresses potential rumors directly—“We have never been acquired,” “Our headquarters is in [City].” Vague denials don’t work.
- Build consensus around your brand. Fix outdated information on your site and online profiles. You need other sites to corroborate your story, too.
- Publish detailed “how it works” pages. Make them specific enough to outcompete third-party explainers in AI-generated answers.
- Claim specific superlatives. Stop saying “industry-leading.” Own claims like “fastest at [metric]” or “best for [use case].” Specific claims are quotable; generic ones aren’t.
- Monitor for narrative hijacking. Set alerts for your brand name plus words like “investigation,” “insider,” “lawsuit,” or “controversy.”
4. Work toward becoming a recognized entity
When your brand becomes an entity in Google’s Knowledge Graph, you get a major trust boost. How to work toward this:- Create and verify your Google Business Profile.
- Get mentioned on authoritative sites in your industry.
- Keep your business name, address, and phone number consistent everywhere. This is crucial for local businesses—you can read more about local citations in this guide.
- Build a presence on relevant social platforms.
- Create a Wikidata entry if possible.
5. Help machines read your content with schema markup
Schema markup is structured data that tells search engines exactly what your content means. Instead of making Google guess what “20 minutes” refers to in your recipe, you can explicitly mark it as cooking time.
- Article schema. For blog posts (tells search engines the author, date, topic).
- HowTo schema. For step-by-step guides (perfect for AI systems that love structured instructions).
- FAQ schema. For questions and answers (directly feeds AI the Q&A pairs they need).
- Product schema. For products (includes price, reviews, availability).
- Server-Side Rendering. Render pages on the server to include structured data in the initial HTML response.
- Static HTML. Use schema markup directly in the HTML to limit reliance on JavaScript.
- Prerendering. Offer prerendered pages where JavaScript has already been executed, providing crawlers with fully rendered HTML (consider tools like Prerender.io).
6. Structure content so machines can extract it
Semantic search rewards content that’s easy to understand, well-structured, and clear at a glance. Most importantly, each section of your content should make sense on its own—this is called atomic content. Start with the answer, then add context and explanation. This matters because both readers and AI systems focus most on the beginning of a section and often scan or extract content without reading the whole page.
Further reading
7. For local businesses: map every entity your local business touches
If you run a local business, there’s a simple opportunity that often gets missed. My colleague, Despina Gavoyannis, noticed it while working with local service businesses, and once they fixed it, many of them more than tripled their organic traffic from Google.
Further reading
