Semantic search is search technology that interprets the meaning and intent behind a query instead of matching keywords word for word. By reading context, synonyms, and the relationships between concepts, it returns results that answer what a person actually meant, even when their wording is vague or unusual.
Modern engines break a query into entities — people, places, products, ideas — and map them against a knowledge graph and a web of related concepts. Google's shift began with the Hummingbird update in 2013 and accelerated through language models like BERT and MUM, which read a full sentence rather than isolated terms. Vector embeddings let the engine measure how close two pieces of text are in meaning, so "how do I get red wine out of a rug" can match a page titled "removing wine stains from carpet" with no shared keywords at all.
Repeating an exact phrase ten times no longer helps; covering the concept clearly does. Answer the questions a searcher would naturally ask next, use plain synonyms, and structure a page around a topic rather than a single keyword. A guide on "beginner running shoes" that also explains pronation, cushioning, and sizing satisfies semantic intent far better than one that just repeats the target phrase. Writing for meaning is now the baseline for on-page SEO.
Ambiguity is where this really shows. Take the query "jaguar top speed": the engine reads recent context and the wider session to decide whether you mean the animal or the sports car, then surfaces pages matching that resolved intent instead of every page that happens to contain both words.