What is semantic search?

Semantic search refers to search methods that interpret the meaning of a query instead of only matching its wording against documents. Google identifies entities, their relationships, and the search intent behind the query and delivers results that answer the question even if they use different words. Semantic search is the foundation of modern Google Search and of AI search.

How does semantic search work?

Semantic search translates language into meaning. Three building blocks work together:

• Entities: Google captures uniquely identifiable things such as people, places, brands, and concepts as entities and stores their relationships in the Knowledge Graph.
• Search intent: The algorithm assigns a goal to every query, such as informing, comparing, or buying. Results that miss the search intent do not rank, no matter how often the keyword appears.
• Language models and vectors: Words and texts are represented as numeric vectors; similar meaning means proximity in vector space. That is how Google recognizes that “car” and “automobile” mean the same thing.

Google built this capability in stages: the Knowledge Graph (2012) brought entity knowledge, the Hummingbird update (2013) put the meaning of the whole query at the center, RankBrain (2015) added machine learning for unseen queries, and BERT (2019) brought the understanding of how individual words work in context.

What sets semantic search apart from keyword search?

Keyword search matches strings, semantic search answers questions. The difference at a glance:

For a keyword, this means: it remains the entry point of your research, but it no longer wins through repetition. It wins through content that fully answers the question behind it.

Are LSI keywords part of semantic search?

No. Google does not use Latent Semantic Indexing. LSI is an analysis technique from the 1980s built for small, static document collections; Google spokespeople have clarified repeatedly that it plays no role in Google Search. The popular term LSI keyword is a marketing word, not a Google mechanism.

Only this much is right about the core of the idea: thematically related terms belong in a good text because they make the topic complete. They work through the meaning analysis of semantic search, not through an LSI formula.

What does semantic search mean for SEO?

Semantic search shifts optimization from the word to the topic. Four consequences for practice:

1. Search intent before keyword: The content serves the goal behind the query, not the string.
2. Name entities clearly: Unambiguous names, consistent terms, and clear statements make content machine-readable.
3. Cover topics completely: Connected content in clusters beats isolated single pages.
4. Deliver structured data: Structured data in JSON-LD format translates content into the entity format that search engines and AI systems process directly.

AI search pushes this principle further: AI Overviews, ChatGPT, and Perplexity preferentially cite content that is semantically unambiguous and citable, the field of optimization for AI visibility. These are exactly the principles we follow in our ongoing SEO services.

Frequently asked questions about semantic search

Since when has Google used semantic search?
Since 2012, in stages: the Knowledge Graph brought entity knowledge, the Hummingbird update of 2013 put the whole query instead of single words at the center, and RankBrain (2015) and BERT (2019) added machine learning and context understanding.

What is an example of semantic search?
The query “Who invented the printing press?” returns Johannes Gutenberg as the answer, although many of the ranking pages do not contain the exact wording of the question. Google answers the meaning of the question, not its wording.

What does semantic search have to do with AI search?
AI search builds on semantic search: language models process meaning, entities, and relationships. Content with a clear semantic structure is therefore also cited preferentially by AI Overviews, ChatGPT, and Perplexity.