Semantic search: beyond keywords to meaning

What is semantic search and how does it work?

Semantic search is a search technology that attempts to understand the meaning behind a search query, rather than simply matching exact keywords. When you search for "how do I prevent my plants from dying in winter," a semantic search system understands that you have questions about plant care in cold months, even if a relevant article does not literally contain the word "dying" but speaks about "winter hardiness" or "frost protection."

This shift from lexical matching (exact words) to semantic matching (meaning) is one of the most fundamental changes in the history of search technology. Google's BERT update in 2019 and the subsequent MUM update marked the beginning of this transition for traditional search engines. AI models like ChatGPT and Perplexity are semantic by nature: they understand language at a deeper level than keyword matching.

For anyone working on Answer Engine Optimization, this is a fundamental insight. Optimizing for AI models does not mean repeating keywords, but fully and clearly answering the question behind the search query. This requires a different approach to content creation than the keyword-focused approach that dominated SEO for years.

From keywords to entities and concepts

In the world of semantic search, keywords have been replaced by two more powerful concepts: entities and topics. An entity is a unique, identifiable thing: a person, a company, a place, a concept. A topic is a coherent knowledge area that encompasses multiple entities and related concepts.

When Google or an AI model analyzes your content, they do not search for keywords but for entities and the relationships between them. This is precisely why Schema.org markup is so powerful: it makes the entities in your content explicit and machine-readable, allowing semantic search systems to better understand and classify your content.

Take the example of an article about "sustainable energy in the Netherlands." A keyword-focused approach would focus on repeating variations like "sustainable energy," "green energy Netherlands" and "renewable energy." A semantic approach focuses on fully covering the relevant entities (solar panels, wind farms, hydrogen, government subsidies, climate agreements) and their interrelationships. The AI model then understands that your article thoroughly covers the topic "sustainable energy in the Netherlands," even if specific keywords are missing.

How AI models determine semantic relevance

AI models use vector embeddings to capture the semantic meaning of text. Every sentence, paragraph or document is converted into a mathematical vector: a series of numbers that represent meaning in a multidimensional space. Texts with similar meaning have vectors that are close together, regardless of the specific words used.

# Simplified example of semantic similarity\n# (in reality, vectors are much larger)\n\nQuery: "How do I protect my website from hackers?"\nVector: [0.82, 0.15, 0.91, 0.33, ...]\n\nArticle A: "Website security: 10 steps against cyber attacks"\nVector: [0.79, 0.18, 0.88, 0.35, ...]\nSimilarity: 0.96 (very high, gets cited)\n\nArticle B: "Hackers use new techniques in 2026"\nVector: [0.45, 0.72, 0.31, 0.88, ...]\nSimilarity: 0.42 (low, does not get cited)\n\n# Article A gets cited despite not containing the word "hackers"\n# because the semantic meaning matches

This mechanism explains why some pages are cited even though they do not contain the exact search terms, and why other pages that do contain the search terms still are not cited. The semantic match is more important than the lexical match.

Writing content for semantic search

Writing content that performs well in semantic search requires a fundamentally different approach than keyword optimization. Instead of focusing on repeating specific words, you focus on fully covering a topic with all relevant aspects, synonyms and related concepts.

Topical completeness as starting point

The most important factor for semantic relevance is topical completeness: the degree to which your content covers all relevant aspects of a topic. An article about "robots.txt for AI" that only explains what robots.txt is but does not address specific AI crawlers, user agents, crawl delay and the relationship with sitemap.xml, is semantically incomplete. AI models prefer content that examines a topic from all angles.

• Identify all subtopics that belong to your main topic and cover them, even if briefly.
• Use synonyms and related terms naturally. If you write about "readability," also mention "comprehensibility," "reading ease" and "Flesch score."
• Answer related questions that readers might have, even if they are not directly in your title.
• Draw connections to related topics and use internal links to make those connections explicit.
• Avoid artificially padding text with irrelevant information; topical completeness does not mean everything needs equal depth.

The readability of your content also plays a role in semantic search. Clearly written content with clear sentence structures is better understood by AI models. Complex, convoluted sentences make it harder for the model to extract the core message, even if the information is factually correct.

Heading structure and semantic segmentation

Your heading structure is not only important for readability but also for semantic segmentation. AI models use headings to divide your content into thematic segments. Each H2 section is treated as a separate semantic block. This means that a well-structured heading hierarchy makes it easier for AI models to find the specific segment relevant to a search query.

# Good semantic heading structure\n\nH1: Sustainable energy in the Netherlands: the complete guide\n  H2: What is sustainable energy?\n    H3: Difference between renewable and sustainable\n  H2: Solar energy in the Netherlands\n    H3: Rooftop solar panels\n    H3: Solar farms\n  H2: Wind energy: onshore and offshore\n  H2: Hydrogen as energy carrier\n  H2: Subsidies and regulations\n    H3: SDE++ subsidy\n    H3: Net metering scheme\n\n# Each H2 section can be independently cited\n# as an answer to a specific question

Semantic search and structured data

Structured data via Schema.org is the most powerful instrument to help semantic search systems understand your content. While the text on your page provides natural language for human readers, Schema.org provides explicit, unambiguous data for machines.

A combination of semantically rich text and detailed Schema.org markup creates a double layer of comprehensibility. The AI model can interpret your content through both the text and the structured data. This makes your content more robust: even if the model struggles with a complex sentence construction, the structured data provides a fallback for correct interpretation.

The future: multimodal semantic search

Semantic search is rapidly evolving beyond text. Multimodal AI models like GPT-4o and Gemini can understand meaning in text, images, video and audio simultaneously. This means that the semantic relevance of your content will in the future not only be determined by your text, but also by your images, diagrams, videos and other media.

For content creators, this means that alt texts, captions and image file names must be semantically relevant. An image with the filename "IMG_4523.jpg" and alt text "photo" contributes nothing to the semantic richness of your page. The same image with the filename "solar-panels-flat-roof-installation.webp" and a descriptive alt text strengthens the semantic signal of your content.

In semantic search, the page with the most keywords does not win. The page that best understands what the searcher wants to know and provides the most complete answer does.

Key takeaways

• Semantic search understands the meaning behind search queries instead of just matching exact keywords.
• AI models use vector embeddings to determine semantic similarity; topical completeness is more important than keyword density.
• Write content that fully covers a topic with all relevant aspects, synonyms and related concepts.
• A good heading structure helps AI models semantically segment your content and select the right fragment.
• The combination of semantically rich text and Schema.org markup creates a double layer of comprehensibility for AI systems.

Frequently asked questions

Semantic search is the bridge between how people think and how machines understand. Those who write content for meaning rather than keywords build lasting visibility.