Semantic search is a search method that interprets the meaning and intent of a query and returns results that match that meaning, even when they use different words. Lexical or keyword search, by contrast, compares character strings. In SEO the term describes how Google understands queries, entities, and the relationships between them.
At a glance
- Type
- search method that compares meaning instead of character strings
- Approaches
- knowledge graph (entities) and embeddings (vectors)
- At Google
- Knowledge Graph (2012), RankBrain (2015), neural matching (2018), BERT (2019)
- Counterpart
- lexical search (word matching)
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How does semantic search work?
Two approaches are common, and many systems combine them.
Knowledge graphs store entities (people, places, products, concepts) and the relationships between them. Google introduced its Knowledge Graph in May 2012 under the motto “things, not strings.” At launch Google said it contained more than 500 million objects and more than 3.5 billion facts about and relationships between them. One goal was to resolve ambiguous queries. Google’s example was “Taj Mahal,” which can mean the monument, a musician, or a casino in Atlantic City.
Embeddings turn text into numeric vectors that represent its meaning. Texts with similar meaning end up close to each other in vector space. The query is converted into a vector as well, and the system returns the documents whose vectors are nearest, often measured with cosine similarity. Enterprise search tools, site search, and AI assistants that look up sources before answering use this pattern, usually backed by a vector database.
Semantic search at Google
In a 2022 post, Google described several AI systems that changed how Search understands language. RankBrain (2015) was the first deep learning system in Search and relates words to concepts. Neural matching (2018) relates queries to pages. BERT (2019) helps Google understand how combinations of words express different meanings and intents. MUM, according to the same post, was not used for general ranking at the time and served specific applications.
What does it mean for SEO?
Repeating every phrasing of a keyword is less necessary, because search engines recognize synonyms and related terms. What gains weight is covering a topic completely, naming entities clearly (brand, product, place, person), and resolving ambiguity in the text. Structured data can describe entities on a page in machine-readable form. These conclusions are a professional assessment and not a requirement published by Google.
Example
A user searches for “boots for long hikes with a heavy pack.” A lexical search returns pages that contain those words. A semantic search recognizes the need behind the query and can also return a page about “backpacking boots with a stiff midsole,” although none of the query words appear there.
FAQ
What is the difference between lexical and semantic search? Lexical search matches words and strings. Semantic search matches meanings, for example through a knowledge graph or vector similarity.
Does ChatGPT use semantic search? According to reports, assistants that answer with sources typically retrieve documents first and then generate an answer. Embedding-based retrieval is a common way to do the first step. Providers rarely document the details.
What is semantic SEO? An umbrella term for optimizing content around topics and entities. In practice that means complete coverage of a topic, clear references to entities, and structured data where it fits.
Sources
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