Search used to be much easier to explain. Someone typed a phrase into Google, a search engine looked through its index for pages containing those words, and marketers researched keywords, created pages around them, and tried to convince the search engine that their page was the best match.
That description was never quite as simple as it sounded, but today it is especially incomplete.
Modern search systems do not just evaluate strings of words. They work to understand meaning, context, relationships, intent, entities, and the broader task behind a search. A person might type a traditional Google query, ask a multi-part question in Google AI Mode, continue a research session in ChatGPT, compare companies in Perplexity, or ask Gemini a conversational follow-up. The wording may change significantly while the underlying need remains the same.
That is where semantic search comes in. It is also one of the foundations of a strong Modern Search content strategy, because visibility increasingly depends on how clearly your content communicates meaning, relationships, expertise, and relevance across an entire subject.
Semantic search is the process of understanding what someone means rather than relying only on the exact words they use. For marketers, that changes the goal. Instead of creating a page that matches one keyword, we need to create information that search systems can confidently connect to a subject, an audience, a problem, related concepts, and the next questions someone is likely to ask.
The term semantic SEO is still commonly used to describe this work, but it is more useful today to view it as part of the larger Modern Search Marketing landscape. Semantic understanding is not a separate optimization trick. It is one of the foundations connecting traditional organic search, AI-generated search experiences, content strategy, brand authority, entity recognition, and LLM visibility.
In other words, semantic search is less about getting better at inserting related keywords and more about becoming easier to understand.
What Is Semantic Search?
Semantic search refers to a search system’s ability to interpret the meaning and context of a query and connect it to relevant information, even when the wording on the page does not exactly match the wording used by the searcher.
Consider a search such as “best shoes for rocky desert trails in summer.” A search system has more to interpret than the individual words. It needs to recognize that “shoes” probably refers to trail-running or hiking footwear, depending on the context, “rocky” indicates terrain, “desert” suggests heat, dust, traction, and potentially sharp surfaces, and “summer” adds temperature and breathability considerations. The searcher may also care about cushioning, durability, grip, foot protection, and how the shoe performs over longer distances.
A useful result does not need to repeat “best shoes for rocky desert trails in summer” throughout the page. It needs to demonstrate an understanding of the problem.
That is the difference between keyword matching and meaning matching.
Google has been moving in this direction for years. Its Knowledge Graph was introduced around the idea of understanding “things, not strings,” connecting real-world entities and their relationships rather than treating every query as an isolated set of characters. Google’s original explanation of the Knowledge Graph remains a useful reference for understanding that shift.
Today, the concept extends much further. Modern search involves machine-learning systems, natural-language understanding, knowledge systems, retrieval technologies, and generative AI working together to identify information that best satisfies a person’s need.
Is Semantic SEO Still a Useful Term?
Yes, but with an important caveat.
Semantic SEO traditionally described the practice of optimizing content around meaning, related concepts, search intent, and topical relationships rather than focusing narrowly on a primary keyword. That basic idea is still useful, but the environment around it has changed considerably.
Semantic understanding now affects far more than whether a webpage earns a traditional organic ranking. It can influence whether a page is considered relevant to a complex search, whether a passage is useful during AI-powered retrieval, whether a brand is clearly associated with a category, whether related content is understood as part of a larger topic, and whether information can contribute to an AI-generated answer.
For that reason, we would not recommend building a separate “semantic SEO strategy.” We would build a Modern Search strategy that is semantically clear by design.
Your website, content, and broader digital footprint should make a few fundamental things easy to understand: what the page is about, who it is for, what problem it solves, which entities are involved, how the subject relates to other topics, what evidence supports the claims, and why the source deserves to be trusted.
Those questions are much more important than calculating how many synonyms should appear on a page.
Keywords Still Matter. They Just Have a Different Job.
Moving beyond old-school SEO does not mean throwing keyword research away.
Keywords remain one of the clearest sources of information about how people express demand. Search volume, query patterns, modifiers, questions, comparisons, and long-tail searches can all tell you what an audience cares about. The mistake is treating each keyword as an independent target.
Modern keyword research should help uncover search behavior, language patterns, needs, and topic relationships rather than simply creating a spreadsheet of phrases that each need it’s own page.
Suppose a software company finds searches such as “document processing software,” “automated document processing,” “AI document extraction,” “extract data from PDFs automatically,” “invoice data extraction software,” and “document automation platform.”
An older approach might create six highly similar pages to target each phrase individually. A semantic approach asks a better question:
Are these six different needs, or six ways people describe overlapping parts of the same problem?
Sometimes, separate pages are appropriate because the intent truly differs. Other times, one strong resource can address the larger subject while supporting pages explore distinct use cases, industries, features, or buying decisions.
This is why understanding search intent remains so important. Keywords tell you what people type. Intent helps explain what they are trying to accomplish.
The Building Blocks of Semantic Search
There is no single “semantic ranking factor” that marketers can optimize. Semantic understanding is better thought of as the result of multiple information signals working together.
For content teams, several concepts matter most.
Search Intent and the Task Behind the Query
Search intent is often divided into informational, commercial, transactional, and navigational categories. Those classifications are useful, but modern search frequently gets messier.
Someone asking “Is HubSpot worth it for a 20-person B2B company?” may be researching, comparing products, evaluating costs, looking for firsthand experiences, and preparing to purchase at the same time.
The content, therefore, needs to do more than recognize “HubSpot” as the keyword. A strong page might discuss pricing, company size, implementation requirements, CRM needs, alternatives, expected use cases, limitations, and who should or should not consider the platform.
That is semantic depth driven by user need. The closer your content gets to solving the actual task, the less important it becomes to obsess over matching one exact phrase.
Entities
An entity is a distinct person, organization, product, place, concept, or other identifiable thing. Entities matter because language is full of ambiguity.
“Apple” can refer to a fruit or a technology company. “Phoenix” might refer to a city, a mythological bird, or any number of businesses and products. Context helps a search system determine which meaning is intended.
For a business, entity clarity means consistently communicating things such as the company name, products and services, locations, founders and subject-matter experts, industries served, areas of expertise, customer categories, and related organizations.
A business should become increasingly difficult to misunderstand as a search system gathers more information about it.
That clarity cannot come from a single schema tag or an optimized About page. It is created through the cumulative consistency of your website, company profiles, authorship, reviews, citations, social properties, industry publications, business listings, and other credible references across the web.
Relationships Between Entities
Knowing that two entities exist is useful. Understanding their relationship is even more useful.
Imagine a website mentions Cadence Search, Gilbert, Arizona, search marketing, AI Optimization, technical SEO, and B2B SaaS.
Those are individual entities and concepts.
Now compare that with:
Cadence Search is a search marketing agency based in Gilbert, Arizona, that provides technical search strategy, content, authority development, and AI search optimization for companies, including B2B and SaaS organizations.
The second version provides structure. It explains what the company is, what it does, where it is located, and who it serves.
That type of clarity is useful for both humans and machines. Semantic optimization should almost always make information clearer, not more robotic.
Context
Context gives words meaning.
If a website discusses “conversion,” are we talking about e-commerce purchases, B2B lead generation, file formats, religious conversion, or something else entirely?
A well-developed page naturally provides sufficient context to answer that question. This is one reason thin pages often struggle. They may technically mention a target phrase but provide very little information that establishes the boundaries, relationships, or implications of the subject.
Context can come from definitions, examples, attributes, comparisons, supporting concepts, data, internal links, and clear site architecture.
Topic Relationships
A website is not simply a collection of independent URLs. The relationships between those URLs help both people and machines understand how information fits together.
For example, a complete search marketing ecosystem might connect:
Modern Search Marketing → Content Marketing → Search Intent → Keyword Research → Semantic Search → AI Search Optimization → Authority Development → Measurement
Each page can answer a distinct question while supporting the larger subject.
This is why we increasingly think in terms of topic territories and content ecosystems instead of simply “keyword clusters.” Our guide to Content Marketing for Modern Search goes deeper into building content around these broader relationships.
How AI Search Makes Semantic Understanding Even More Important
Generative search has made the importance of meaning much more obvious.
Traditional search often begins with a single query and returns a set of results. AI-powered search can take a more complex question, break it into smaller information needs, retrieve information from multiple sources, and synthesize it into a response.
Google has explained that AI Overviews and AI Mode can use a process called query fan-out, where related searches are issued across different subtopics and data sources to help build an answer. Google’s documentation on AI features in Search provides more detail on how these experiences work.
Imagine someone asks:
“What is the best search marketing strategy for a B2B SaaS company entering the enterprise market with a small internal marketing team?”
There are many questions hiding inside that one prompt. What changes when a SaaS company targets enterprise buyers? Which search channels matter? What content does an enterprise buyer need? How should AI search be considered? What resources can a small internal team realistically manage? Should the company hire an agency? How should results be measured?
This is much closer to the way people actually think.
It also explains why content built around one exact phrase can be limiting. A strong resource needs enough clarity and depth to be useful across the related information needs surrounding a topic.
For marketers specifically interested in this shift, our guide to Artificial Intelligence Optimization and AI Search Optimization explores how content is discovered, understood, retrieved, and cited in AI-powered environments. We also break down the terminology in our SEO vs. GEO guide.
The important point is that semantic search and AI search are not separate universes. AI has simply made the transition from matching queries to understanding information needs much harder to ignore.
Semantic Search Is Not About LSI Keywords
One concept from the older SEO conversation deserves to be retired: LSI keywords.
For years, SEO articles encouraged marketers to find “LSI keywords” and sprinkle them throughout content. The phrase became a loose term for related terminology, synonyms, and concepts.
That is not a useful modern optimization strategy.
You do not need a tool to provide a list of semantically related words so you can force them into a page. Modern search systems are considerably better at understanding relevance even when a page does not contain an exact match for every possible query variation. Google’s current guidance for generative search specifically advises against creating excessive content for every variation or fan-out query and notes that its systems can understand relevant pages even without exact-word matches. Google’s guidance for AI search optimization reinforces this point.
Natural topical language is still valuable, of course. A comprehensive article about trail running shoes will likely cover traction, cushioning, outsole, terrain, heel drop, rock plates, and durability, as these concepts genuinely belong in the discussion.
That is very different from adding terms because an SEO tool gave them a “semantic score.”
Write with enough expertise and specificity to explain the subject correctly, and relevant terminology will usually follow.
Semantic Search Is Not a Word-Count Competition Either
Another old idea we would leave behind is the belief that semantic optimization requires creating extremely long pages.
Depth matters. Word count does not.
A 700-word article that completely answers a narrow question can be far more useful than a 4,000-word article padded with repetitive explanations. Conversely, a complex topic may genuinely require several thousand words to cover responsibly.
Google explicitly states that it does not have a preferred word count and recommends creating substantial, complete information because it helps the reader, not because hitting a certain length earns a ranking advantage. Google’s people-first content guidance is particularly clear on this.
So instead of asking, “How long does this article need to be?”, ask:
“What does someone need to understand before this page has actually done its job?”
Then stop when the answer is complete.
How to Build Content for Semantic Search
The strongest semantic search strategy begins before anyone writes a page. It starts with understanding the information environment surrounding a topic.
Start With the Audience, Not the Keyword
Keyword data should be one input, not the entire brief.
Look at Search Console queries, sales conversations, customer-service questions, reviews, community discussions, competitor content, internal site search, product documentation, traditional search results, and AI search responses.
You are looking for recurring problems, questions, objections, terminology, and decision points.
This is the difference between targeting search volume and understanding search demand.
Define the Core Topic and Its Boundaries
Every page should have a clear job.
If the page is about semantic search, determine what belongs in that discussion and what deserves a separate supporting resource.
Semantic search overlaps with search intent, structured data, AI search, keyword research, and information architecture. That does not mean one article needs to become a complete guide to all five.
It means the article should explain how those ideas connect and then use internal links to let readers continue along the appropriate path.
Identify Important Entities and Attributes
Ask which people, products, organizations, categories, locations, technologies, and concepts someone needs to understand. Then determine which characteristics matter.
For a product that might include price, material, compatibility, dimensions, use case, and manufacturer. For a service business, it could include services, industries, geographic reach, expertise, methodology, and team. An informational topic may include definitions, applications, limitations, examples, related concepts, and common misconceptions.
You do not need to build an “entity spreadsheet” for every blog post. The point is to ensure important relationships are explained rather than assumed.
Build Information Around Questions, Not Keyword Variations
Related questions are valuable because they expose different aspects of the searcher’s problem.
Someone researching semantic search may want to know what semantic search is, whether semantic SEO is still relevant, what entities are, how schema fits in, how semantic search relates to AI, whether LSI keywords are still used, and how to optimize content around meaning.
Those questions do not necessarily require separate articles.
Several belong naturally on this page. Others could justify allocating separate resources if the subject becomes sufficiently deep.
The strategy should follow the information need, not the number of keywords available.
Use Internal Links to Build a Content Graph
Internal links do more than distribute authority between pages. They help people and crawlers discover related information while communicating which resources belong together.
The anchor text matters because it provides context. “Learn more about our approach to AI search optimization” is more informative than “click here.”
Internal linking should therefore be intentional but natural. A page should connect to the next resources someone would reasonably want based on what they are currently reading.
That creates a site where information behaves more like a network than a pile of independent URLs.
Make Important Answers Easy to Extract
Writing for semantic search does not mean writing for robots. It does mean removing unnecessary ambiguity.
When a section asks “What is semantic search?” answer the question clearly near the beginning of that section. When comparing two ideas, make the distinction obvious. When making a claim, explain the evidence. When a list genuinely improves comprehension, use one. When a concept requires nuance, give it enough space.
This improves the experience for readers and can make individual passages easier for search and AI systems to identify when they are retrieving information for a narrower question.
Add Original Information Whenever You Can
Search systems already have enormous amounts of generic information.
Repeating the same summary found on twenty other websites does not create much reason for your version to become the preferred source.
Originality can come from proprietary data, customer research, expert commentary, firsthand experience, screenshots, examples, experiments, case studies, frameworks, templates, or a defensible point of view.
Google’s people-first content guidance specifically encourages original information, research, analysis, and substantial value beyond what other sources already provide.
In practical terms, make your content harder to replace.
That is good advice for traditional search, AI search, and actual human readers.
What Role Does Structured Data Play?
Structured data is useful, but it is frequently oversold.
Schema markup provides machine-readable information that helps search engines understand and classify content on a page. Google describes structured data as a way of providing explicit clues about page meaning and uses it to support eligible enhanced search experiences. Google’s structured data documentation explains that relationship in detail.
That makes structured data relevant to semantic clarity.
What it does not mean is:
Add schema → receive higher rankings.
Google makes it clear that properly implemented structured data does not guarantee that a rich result will appear, and that structured-data issues affect rich-result eligibility rather than serving as a shortcut to higher traditional rankings.
Use structured data to accurately describe information that actually exists on the page. Depending on the site, that may include information about an organization, article, product, event, local business, author, video, or another supported content type.
Think of schema as a layer of clarification, not a secret ranking switch.
Your Technical Foundation Still Matters
Semantic clarity does not help much if important content cannot be reliably discovered or accessed.
Modern search strategy still depends on strong technical fundamentals such as crawlability, indexability, canonicalization, site architecture, internal linking, mobile usability, and accessible page content.
This becomes even more important as the search ecosystem expands. Google may be capable of rendering complex JavaScript, for example, but that does not mean every crawler, retrieval system, or AI platform processes a website in exactly the same way.
When practical, make important information easy to access.
Our guide to technical SEO and modern search infrastructure explores this in more depth.
Google also states that pages generally need to be indexed and eligible to appear in Search before they can be shown as supporting links in its AI features. There is no special technical loophole that allows a poorly accessible website to skip the fundamentals and jump directly into AI visibility.
The technology has changed. Good web architecture has not stopped mattering.
Brand Authority Is Part of Semantic Understanding Too
Your website does not exist in isolation.
Search systems can encounter information about your organization across publications, review sites, directories, social networks, industry organizations, customer websites, podcasts, videos, forums, and many other sources. Those references can provide useful context about who you are and what you are known for.
If a cybersecurity company claims on its own website to be a leader in cloud security, that is one data point. If respected security publications, conference organizers, analysts, customers, and industry experts repeatedly associate the company with cloud security, the broader digital footprint becomes much more informative.
This is why authority development and digital PR increasingly overlap with entity clarity.
The goal is not merely to earn backlinks. It is to create a credible web of evidence around the organization and its expertise.
For AI-driven discovery in particular, that broader footprint can matter because the answer to “What does this company do?” may be informed by information far beyond the company’s homepage.
Semantic Search and Topical Authority
“Topical authority” is another term that gets used loosely.
It should not be treated as a documented Google score that rises whenever you publish more articles. The useful concept behind topical authority is much simpler:
Does your website demonstrate meaningful expertise and coverage around the subjects it has a legitimate reason to discuss?
A financial software company that publishes high-quality information about accounting automation, invoicing, reconciliation, financial workflows, and related software decisions is developing a coherent body of information.
If the same company suddenly publishes fifty articles about unrelated subjects simply because the keywords look easy, the connection becomes much harder to justify.
Coverage should follow real expertise and audience needs. The objective is not to publish everything remotely connected to a category. It is to build the most useful information ecosystem your organization is capable of creating.
This is also why our approach to modern content marketing focuses on topic territories rather than random publishing volume.
Semantic Search Changes How We Think About Content Architecture
One of the biggest practical changes is how we organize websites.
Older content strategies often created a new URL whenever a keyword tool uncovered another phrase. That can produce overlapping articles, search cannibalization, thin pages, repeated explanations, weak internal linking, confusing site architecture, and large volumes of content nobody really needs.
A stronger approach determines which page should be the authoritative resource for a subject and then creates supporting content only where a genuinely different need exists.
Think:
Topic → subtopic → question → decision → action
rather than:
Keyword → URL → next keyword → next URL
This results in fewer orphaned pages and clearer relationships between information. It is also more sustainable because your team can maintain and improve a coherent knowledge base rather than trying to keep hundreds of nearly identical keyword pages up to date.
How Should Semantic Search Be Measured?
You cannot measure semantic performance with one metric.
Tracking the ranking of a primary keyword can still be useful, but it tells only a small part of the story. Instead, look at performance across a broader topic.
That may include search impressions across related queries, organic traffic to the content ecosystem, the number and variety of queries driving visibility, supporting-page performance, assisted conversions, qualified leads, brand search growth, non-brand category visibility, AI citations and mentions, referral traffic from emerging search platforms, and whether AI systems accurately describe your organization.
This requires moving from keyword reporting to visibility reporting.
A page may lose ranking for one exact phrase while gaining visibility across dozens of more specific searches. Another page may receive fewer clicks from an AI-powered result but become a frequently cited source, increasing brand discovery.
Neither outcome is captured particularly well by a single position number.
For companies expanding beyond traditional organic reporting, our LLM and AI search optimization services focus on that broader visibility environment.
Semantic Search Mistakes We Would Leave Behind
A modern semantic strategy becomes much easier when you stop carrying old SEO baggage.
We would leave behind:
- LSI keyword lists: Use real subject expertise and natural terminology instead.
- Synonym stuffing: Search systems do not need every conceivable variation forced onto the page.
- One URL for every keyword: Separate pages only when the audience’s need or intent actually differs.
- Arbitrary word counts: Write until the subject is complete, then stop.
- Schema as a ranking hack: Use structured data accurately, not as a magic switch.
- Writing for algorithms before people: Search systems increasingly exist to understand what people want, so start there.
- Publishing for topical authority alone: More pages do not automatically equal greater expertise.
- Mass-producing AI summaries: Commodity information becomes easier to create every day and, therefore, easier to replace.
- Treating AI search as an isolated strategy: Traditional search, AI search, content, authority, and technical accessibility are increasingly part of the same ecosystem.
Most of these mistakes stem from the same problem: trying to optimize individual signals rather than improving the overall quality and clarity of the information.
What Does Semantic Search Mean for Modern Search Marketing?
Semantic search represents one of the most important changes in how marketers should think about visibility.
The old mental model was:
Find keyword → create page → optimize keyword → earn ranking
A better modern model looks more like:
Understand audience → define topic → establish context → explain relationships → demonstrate expertise → make information accessible → build authority → earn visibility across search experiences
Keywords remain part of that process. So do rankings, links, technical SEO, and structured data. But none of them should be viewed in isolation.
Search systems are increasingly trying to understand the same thing your potential customer is trying to understand:
Is this information relevant, credible, and useful for the problem in front of me?
That is why the future of semantic search is not about discovering another optimization trick. It is about making your business and its expertise easier to find, interpret, trust, and choose.
Semantic Search Is Really About Being Understood
The phrase “semantic SEO” can make this topic sound much more technical than it needs to be.
At its core, the idea is simple.
People do not think in keywords. They think in problems, questions, comparisons, goals, products, people, places, and relationships. Modern search systems are getting better at operating in much the same way.
Your job is to create a digital presence that clearly explains who you are, what you know, what your products or services do, how ideas connect, and why your information deserves to be considered.
At Cadence Search, we think of that as part of the larger Modern Search Marketing ecosystem.
Traditional organic search still matters. Technical SEO still matters. Content still matters. Authority still matters. AI has not made those foundations irrelevant. It has made their relationships much more obvious.
The brands that thrive will not be the ones that discover the perfect density of semantic keywords.
They will be the ones that are easiest to find, understand, trust, cite, and choose wherever search happens.
If you want to understand how clearly search engines and AI platforms currently interpret your website, talk with Cadence Search about your search strategy.
Frequently Asked Questions About Semantic Search
What is semantic search?
Semantic search is the process of interpreting the meaning, context, and intent behind a search rather than matching results solely to the exact words in the query. It can involve understanding concepts, entities, relationships, attributes, and related information needs.
What is semantic SEO?
Semantic SEO is a marketing term for optimizing website content around meaning, context, entities, search intent, and topic relationships rather than relying exclusively on exact-match keywords. We prefer treating these principles as part of a broader Modern Search strategy rather than as a standalone SEO technique.
Is semantic SEO still relevant?
Yes. In fact, the underlying principles are more relevant as search becomes more conversational and AI-assisted. What has become outdated are tactics such as LSI keyword lists, synonym stuffing, and creating separate pages for every small keyword variation.
What are entities in semantic search?
Entities are identifiable people, organizations, products, places, concepts, or other distinct things. Search systems can use contextual information and relationships to distinguish one entity from another and better understand what a page or query refers to.
Do keywords still matter in semantic search?
Yes. Keywords provide valuable information about audience language and search demand. The difference is that modern content strategies should use keywords to understand themes, questions, and intent rather than treating every phrase as a separate optimization target.
Does schema markup improve semantic search?
Structured data can provide explicit information about a page and help search engines classify certain content. It can support semantic clarity and eligibility for some enhanced search experiences, but schema should not be treated as a direct ranking boost.
How does semantic search relate to AI search?
AI-powered search systems may retrieve and synthesize information from multiple sources and related subqueries. Clear topic coverage, entity relationships, context, useful passages, accessibility, and source credibility can therefore support both traditional search visibility and AI-powered discovery.
What is the difference between semantic search and AI search?
Semantic search refers to the ability to understand the meaning and relationships among information and queries. AI search is a broader search experience that may use generative models, retrieval systems, semantic understanding, and other technologies to research and synthesize answers. Semantic understanding is, therefore, one component of many modern AI search experiences.
Do I need to optimize separately for semantic search?
Usually not. Strong semantic optimization largely comes from creating helpful, clearly structured content, building sensible information architecture, accurately representing entities, maintaining strong technical accessibility, earning credible authority, and addressing your audience’s real needs.
How do I improve my website for semantic search?
Start by understanding audience intent, organizing content around meaningful topic relationships, explaining important entities and attributes, answering questions clearly, using internal links strategically, adding accurate structured data where appropriate, improving technical accessibility, and building credible signals about your brand across the broader web.