Semantic Search

What is Semantic Search?

Semantic search is a search approach that interprets the meaning and intent of a query rather than matching its literal words. It uses context, entities, concept relationships and language models to return results that answer what the person actually wants. A query like “tool to stop phishing emails for a 200-person company” is understood as a need, not a string.

The term borrows from the Semantic Web idea of machine-understandable meaning. In search marketing it describes the long shift from lexical ranking to meaning-based ranking, a shift that AI answer engines have made obvious to everyone.

How it works

Semantic search systems combine several techniques:

• Intent interpretation. Classifying whether a query is informational, commercial or navigational, and what the underlying task is.
• Entity understanding. Recognizing people, products, companies and concepts and how they relate, often via a knowledge graph.
• Query expansion and rewriting. Adding synonyms or reformulating the query, and in AI engines, issuing several related sub-queries.
• Embeddings and vector retrieval. Matching on meaning rather than shared words.
• Language model reading. Models such as BERT, and now large language models, read passages to judge relevance and, in answer engines, compose a response.

From SEO to AEO and GEO

Semantic search is the bridge between classic SEO and AEO. Google’s milestones, including the Knowledge Graph (2012), Hummingbird (2013), RankBrain (2015), neural matching (2018) and BERT (2019), had already pushed SEO from keywords toward topics and intent. AI answer engines take the last step: they not only understand the query, they write the answer and pick a few sources to cite.

Era	What search understood	What SEO optimized
Lexical	Words	Keywords, density
Semantic	Meaning, entities, intent	Topics, intent, entities
Generative	Meaning, then synthesis	Quotable passages, corroboration

What carries over: intent matching, topical coverage, entity clarity, structured data and internal linking. What changed: the page is now a source for a generated answer, so clarity at the passage level and consistency with what other sites say about you matter more.

For B2B SaaS and cybersecurity brands this cuts both ways. Category language is crowded with near-synonyms (XDR, EDR, MDR), so a page that defines its terms precisely and states how they relate is easier for semantic systems to place correctly.

What to do about it

1. Start from questions, not keywords. Build topic maps from real buyer questions in sales and support data.
2. Cover the cluster. One strong page per core question, linked to related pages, beats many thin variants.
3. Name entities precisely. Product names, categories, standards (SOC 2, NIST CSF) and integrations, spelled consistently.
4. Use structured data (Organization, Product or SoftwareApplication, Article) to make entities explicit.
5. Answer directly at the start of each section, then add depth and evidence.
6. Check consistency of how your company is described across your site, docs, review sites and profiles.

Myths

• “Keywords no longer matter.” Exact terms still help, especially for names, codes and acronyms.
• “Synonyms everywhere help semantic search.” Clear writing helps; stuffing variants does not.
• “Semantic search is new.” It has shaped Google for more than a decade.
• “Structured data alone makes a page semantic.” Markup clarifies entities, but the visible text still has to answer the question clearly.

Related terms

• Embeddings
• Vector Search
• Search Intent
• Entity Optimization
• Knowledge Graph
• Hummingbird
• GEO vs SEO

Sources

• What is semantic search?, Google Cloud
• Understanding searches better than ever before (BERT), Google
• What is semantic search?, Elastic