Semantic search is search that works on meaning. Instead of only matching the words in a query against the words on a page, it uses language understanding to interpret intent, synonyms, context and relationships between concepts. Modern web search engines and AI search tools are semantic to a large degree, using a mix of embeddings, entity understanding and language models.
From strings to meaning
Early search engines matched strings: a page ranked for a query largely because it contained the query's words. Semantic search recognizes that 'cheap CRM for a two-person team' and 'affordable customer database for a small business' ask nearly the same thing, and that 'Apple' in a query about laptops is a company, not a fruit.
Several techniques contribute: embeddings that place similar meanings near each other, knowledge graphs that model entities and their relationships, and language models that interpret queries and documents.
What it changes for SEO
Semantic search reduces the value of repeating an exact keyword and increases the value of covering a topic completely and clearly. Pages that define terms, name the entities involved and answer the follow-up questions a reader would have give a semantic system more to match against.
- Write for the question behind the keyword, not the keyword alone.
- Name entities explicitly: products, integrations, standards, roles.
- Cover related subtopics a searcher would expect on the page.
- Use the vocabulary your buyers use, including the informal terms.
Example
One page, many phrasings
Brindlecove, a fictional scheduling tool, has separate thin pages for 'appointment booking software', 'booking app for salons' and 'online scheduler for hair salons'. The pages compete with each other and none of them is very good. Consolidating them into one thorough salon scheduling page, which covers the use case in the language salon owners use, gives a semantic system one strong match for all three phrasings.
Common mistakes
Building a page per keyword variant
Close variants usually share intent. Separate pages split signals and often cannibalize each other.
Assuming exact terms no longer matter
Product names, technical terms and brand names still need to appear on the page. Semantic understanding works alongside literal matching.
Questions
Vector search is one technique used to build semantic search. Semantic search is the broader goal of matching by meaning, which can also involve entities, knowledge graphs and language models.
Google has used language understanding systems for many years, with several named systems over time. Those belong to Google's ranking history; semantic search is the general approach they all serve.
