{"id":21287,"date":"2026-06-09T10:35:58","date_gmt":"2026-06-09T13:35:58","guid":{"rendered":"https:\/\/www.mdmarketingdigital.com\/blog\/?p=21287"},"modified":"2026-06-09T10:36:00","modified_gmt":"2026-06-09T13:36:00","slug":"semantic-search-26jnfg","status":"publish","type":"post","link":"https:\/\/www.mdmarketingdigital.com\/blog\/en\/semantic-search-26jnfg\/","title":{"rendered":"Semantic search: what it is and why it is key in SEO."},"content":{"rendered":"\n

According to a study by SparkToro in collaboration with Semrush, which analyzed 332 million Google searches, 52.65% of searches are informational in nature<\/strong><\/a>. People are not looking for pages; they are looking for answers. And Google has been built precisely for that purpose for years.<\/p>\n\n\n\n

That is the root of the change. SEO based on repeating keywords lost effectiveness because search engines stopped reading words and started reading intent. The mechanism behind that transformation has a name: semantic search<\/strong>.<\/p>\n\n\n\n

Understanding how it works is the starting point for any company that wants to rank strategically and attract qualified traffic. We have already covered it on our blog as part of what it means to build an SEO strategy<\/a> today. Here, we take a deeper look.<\/p>\n\n\n\n

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What is semantic search?<\/h2>\n\n\n\n

Semantic search<\/strong> is a search engine\u2019s ability to interpret the meaning and intent behind a query, rather than looking for exact text matches.<\/p>\n\n\n\n

When someone types \u201chow to reduce a fever quickly?\u201d, Google does not scan for pages containing those exact words in that exact order. It analyzes the context: an informational query with a sense of urgency from a user looking for a practical answer. That determines which results are shown, not a literal match.<\/p>\n\n\n\n

The previous model, keyword matching, paired queries with documents containing the same terms. It worked to a certain extent, but it was easy to manipulate and often failed to satisfy users: two people searching for the same thing using different phrases could receive different results even though they needed the same answer.<\/p>\n\n\n\n

Semantic search closes that gap. It interprets synonyms, variations, user context, and relationships between concepts<\/strong> to deliver results that answer what the user intended to ask, not just how the query was written.<\/p>\n\n\n\n

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How semantic search works in Google<\/h2>\n\n\n\n

What we now call semantic search is the result of multiple layers of technology working simultaneously: artificial intelligence, natural language processing (NLP), and machine learning<\/strong>. Google has spent more than a decade refining this system, with milestones that have continually reshaped the SEO landscape.<\/p>\n\n\n\n

Broadly speaking, the process follows this logic:<\/p>\n\n\n\n

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  1. Query analysis.<\/strong> Google identifies the type of intent behind the search: informational, navigational, transactional, or commercial. Changing the intent type completely changes the results that are displayed.<\/li>\n\n\n\n
  2. User context.<\/strong> Search history, location, device, and language are signals that influence what appears. A search like \u201cagencies near me\u201d returns different results depending on where it is entered, with intent and context working together at the same time.<\/li>\n\n\n\n
  3. Entities and knowledge graphs.<\/strong> Google builds maps of concepts and their relationships. It understands that \u201cBuenos Aires,\u201d \u201cCABA,\u201d and \u201cAutonomous City\u201d refer to the same place, and uses that knowledge to determine which result best serves each search.<\/li>\n\n\n\n
  4. Result selection.<\/strong> With that information processed, the algorithm prioritizes the pages that best satisfy the detected intent and context, regardless of whether they use the exact same words as the query.<\/li>\n<\/ol>\n\n\n\n

    This explains why a page can rank for dozens of keyword variations even if it does not explicitly mention all of them.<\/p>\n\n\n\n

    The role of artificial intelligence in semantic search.<\/h3>\n\n\n\n

    AI is what makes all of this possible at scale. Without it, processing billions of queries with linguistic nuances every day would be unfeasible.<\/p>\n\n\n\n

    Natural language processing (NLP)<\/strong> allows Google to understand human language, including its ambiguities and complex structures. BERT, launched in 2019 and officially announced on Google\u2019s blog<\/a>, was a turning point: it gave the search engine the ability to understand words within their full context rather than in isolation. Later came MUM (Multitask Unified Model)<\/a>, offering a deeper understanding of long-form content and multimodal queries.<\/p>\n\n\n\n

    That same semantic logic is now present in virtual assistants and voice search<\/strong>. When someone speaks to their phone and asks a conversational question, the system interpreting that query is the same one operating behind organic search results. That is why optimizing for semantic search also means optimizing for these formats. If you want to explore the relationship between AI and search visibility further, we have an article on SEO for LLMs<\/a> that approaches the topic from another angle.<\/p>\n\n\n\n

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    Types of semantic search<\/h2>\n\n\n\n

    Within the semantic model, different mechanisms operate depending on the type of query and the user\u2019s context.<\/p>\n\n\n\n

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