How engines work
Semantic search
Semantic search retrieves results by meaning rather than by exact wording, so a page can be returned for a question that shares none of its words. It underpins how answer engines find material to answer from.
In more detail
For writers this removes the need for exact-phrase repetition and puts the weight on covering a subject clearly and completely.
Guides that cover this
Related terms
Vector search
Vector search finds documents by comparing embeddings rather than matching keywords, retrieving passages whose meaning is close to the question. It is the retrieval method behind most answer engines.
Embedding
An embedding is a numeric representation of a piece of text that places similar meanings close together. Embeddings let a system match a question to relevant passages even when they share no words.
Entity
An entity is a distinct thing a system can recognise and reason about — a company, a product, a person, a place. Search and AI systems work with entities rather than strings, so being recognised as one matters.
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