Semantic Search
Semantic search is search that interprets the intent and contextual meaning of a query rather than matching only literal keywords — the dominant search paradigm since BERT (2019).
In context
Semantic search uses neural language models to map both queries and documents into vector embeddings, then matches by meaning. The practical implications for SEO: exact-match keyword stuffing is obsolete, topic depth matters more than keyword density, and pages that comprehensively cover an entity tend to rank for many semantically related queries. Schema markup helps semantic engines disambiguate (which 'Apple'? which 'Toronto'?). All major engines — Google, Bing, ChatGPT, Perplexity, Gemini — are semantic at the core.
A Toronto fertility clinic page covering IVF comprehensively — costs, success rates, process, recovery, alternatives — ranks for 80+ semantically related queries it doesn't explicitly target.
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