Semantic Search

Semantic search is search that tries to understand what a query means and return results that match that meaning, even when they share few or no words with the query. It relies on context, entities and intent, and in modern systems on embeddings, numerical representations of meaning. Google has moved in this direction for over a decade, from the Knowledge Graph (2012) to BERT (2019) and today’s AI search.

How it works

Classic lexical retrieval, such as BM25, scores documents by the words they share with a query. Semantic retrieval converts queries and documents into vectors with an embedding model such as Sentence-BERT, then finds the documents whose vectors are closest, usually by cosine similarity. Knowledge graphs add a second layer, connecting queries to known entities and their attributes.

In practice most systems are hybrid. As MLforSEO’s fuzzy matching guide puts it, modern retrieval runs on two layers at once: lexical matching repairs messy input and handles exact terms, while semantic matching captures paraphrases, synonyms and intent. Results are then often re-ranked by a more expensive model.

How it’s used in SEO and marketing

• Keyword research: grouping queries by meaning, which is the basis of a semantic keyword universe.
• Internal linking: finding pages that are about the same thing even when they use different vocabulary.
• On-site search and RAG: loading page embeddings into a vector database to power site search or a retrieval-augmented assistant.
• Content strategy: writing for topics and entities, with clear terminology, so both lexical and semantic retrieval can find a page.

Example

A query for “cheap flights to the sun in winter” contains none of the words in a page titled “Budget Canary Islands holidays for January”. A keyword match misses it; an embedding-based search places the two close together because they share a meaning. Screaming Frog’s embeddings feature applies the same idea to a crawl, offering semantic search across your own pages.

Limitations and things to watch

• Embeddings can miss exact requirements such as product codes, prices or negations (“not gluten-free”), which is why hybrid retrieval exists.
• Results depend on the embedding model and how text is chunked.
• Close in meaning does not mean useful: semantic similarity is a retrieval signal, while quality and information gain decide what gets chosen.

Learn it on MLforSEO

• What Fuzzy Matching Is, and Where It Fits in SEO
• 10 Ways to Automate Internal Linking with AI
• Web Scraping of Page Content in 3 Easy Steps with Screaming Frog
• What Is a Knowledge Graph? How to Use It for Smarter Keyword Strategy
• Semantic ML-enabled Keyword Research (course)