Semantic search is a search engine’s ability to understand the meaning of a query and of the pages it might return, rather than simply matching words. It lets Google answer “where can I get an MOT on a Sunday near me” with garages that open on Sundays, even if their pages never use that exact phrase.
How semantic search works
Early search engines ranked pages largely on whether the query’s words appeared on them. Semantic search adds layers of understanding. Google identifies entities, the people, places, organisations, products and ideas a query refers to, and connects them through its Knowledge Graph. It knows that “the Tube” in a London search means the Underground, and that “Spurs” usually means the football club.
Language models then interpret how words relate in context. Google’s Hummingbird update in 2013 rebuilt its system to handle whole queries rather than single words, and BERT, introduced in 2019, improved its grasp of small words such as “for”, “to” and “without”, which can change the meaning of a query entirely. Modern systems represent queries and passages as mathematical vectors, so text with a similar meaning sits close together even when the wording differs.
The same approach powers AI search features. When AI Overviews or a chat assistant answers a question, it finds passages by meaning, then summarises them.
Why it matters
Semantic search changes what optimising for a keyword means. Repeating an exact phrase no longer helps and makes text read badly. What counts is whether a page genuinely covers its subject: the subtopics, related questions and specific things that someone knowledgeable would naturally include. A page on “landlord gas safety certificate” that also explains the CP12 record, how often the check is needed, who may carry it out and what to do if a tenant refuses access will serve the searcher far better than one that repeats the headline phrase.
It also suits smaller UK businesses. You do not need separate pages for every variant (“cheap”, “affordable”, “low cost”), which would only compete with each other; one strong page can rank for many phrasings of the same need. And because search engines recognise entities, being clearly identifiable as a real business, with consistent details across the web, helps them connect your site with your brand and location.
Common mistakes
- Writing separate pages for synonyms that share one intent, which splits signals and causes cannibalisation.
- Sprinkling pages with lists of so-called LSI keywords, a label Google does not use, in the belief that synonyms alone signal relevance.
- Covering a topic thinly: answering the headline question and none of the obvious follow-ups.
- Vague copy that never names the specific things it is about, such as products, places, standards or regulations.
- Leaving related pages unlinked, when a connected group makes the overall topic far clearer.
How to act on it
For each important page, list the questions a customer would ask before buying, and answer them in plain language. Check “People also ask” and the top results for subtopics you have missed. Name things precisely, and use topic clusters to link related pages so your coverage of a subject is easy to see. Add structured data where it describes the business or the content accurately.
Then read the page as a newcomer would. If it explains the subject well enough for them to act, it is probably doing what semantic search rewards. Planning topic coverage in this way is central to my content SEO and strategy service.