Semantic search
Semantic search retrieves content based on meaning and intent rather than keyword match, using vector embeddings to compare what the user is asking with what each document is about.
Semantic search is the retrieval paradigm behind every modern AI search engine. Instead of matching exact tokens, the system encodes the query and the corpus as vector embeddings and ranks results by similarity.
How it differs from keyword search
| Keyword search | Semantic search |
|---|---|
| Matches exact terms | Matches meaning |
| Synonyms miss | Synonyms hit |
| Sensitive to phrasing | Robust to phrasing |
| Boolean operators | Natural language |
Implications for content
A page about "headache remedies" can rank for "what to take for a migraine" in a semantic system, even if the word migraine never appears. Conversely, a page that mentions a keyword 50 times but lacks topical depth may fail to retrieve, because its embedding does not actually represent the topic.
What this means for GEO
- Topical authority beats keyword density — cover the subject end-to-end
- Internal linking by topic, not keyword — semantic models pick up topical neighborhoods
- Answer multiple phrasings of a question — natural paraphrase variation expands your retrieval surface
- Use entities, not just keywords — proper nouns, product names, and well-defined concepts anchor an embedding more strongly than generic terms
