What Is Semantic Search?

Semantic search is a class of information retrieval methods that return results based on the meaning and intent of a query rather than on the literal presence of matching keywords. Traditional keyword-based search matches documents to queries by counting term occurrences, which can miss relevant results that use different vocabulary and can return irrelevant results that use the same words in a different sense. Semantic search addresses this by building representations of queries and documents in a shared meaning space, typically a high-dimensional numerical vector space, so that proximity in the space reflects conceptual similarity. The field draws on natural language processing, knowledge representation, and information retrieval, and has moved from ontology-based approaches in the early 2000s to neural embedding approaches since the widespread adoption of transformer models in the late 2010s, a transition surveyed in a 2023 arXiv survey on large language models for information retrieval.

The distinction from keyword search is clearest at the edges of the vocabulary problem: a semantic search system can correctly retrieve a document about "myocardial infarction" in response to a query for "heart attack" because both phrases map to nearby points in embedding space. Context awareness is central to this property; the same word can carry different meanings depending on surrounding words, and models trained on large text corpora capture this polysemy in their representations.

Natural Language Processing and Embeddings

The backbone of modern semantic search is the sentence or passage embedding: a function that maps a piece of text to a fixed-length vector such that semantically similar texts receive similar vectors. Early approaches used bag-of-words models extended with latent semantic analysis (LSA), which applied singular value decomposition to term-document matrices to identify latent topics. Word2Vec and GloVe, introduced between 2013 and 2014, produced dense word vectors from large corpora, but averaged word vectors poorly at capturing sentence-level meaning. Transformer-based models, particularly BERT and its descendants, improved substantially on this by computing context-dependent representations: every token's embedding incorporates information from the full surrounding sentence. Fine-tuned sentence encoders such as Sentence-BERT optimize directly for semantic similarity, producing embeddings suited to dense retrieval. Research on vector search and semantic embeddings for document retrieval published on arXiv demonstrates that BERT-based models achieve precision values in the range of 0.98 to 0.99 on retrieval benchmarks, though query latency increases with embedding dimensionality.

Query Processing and Retrieval

In a deployed semantic search system, query processing involves two stages. In the first stage, an encoder converts the user's query into a vector. In the second stage, an approximate nearest-neighbor algorithm finds the documents in the index whose vectors are closest to the query vector, measured by cosine similarity or dot product. Libraries such as FAISS and HNSWlib provide efficient approximate nearest-neighbor indexing over millions of vectors. Ontologies and structured knowledge graphs can augment this pipeline by adding entity recognition and query expansion: when a query contains a named entity, the system can retrieve synonyms, broader terms, or related concepts from the ontology and incorporate them into the search. This hybrid approach, combining neural embeddings with structured symbolic knowledge, is common in enterprise search systems where domain terminology is specialized and coverage matters. A practical implementation is described in the CERN database blog on building a semantic search engine with vector databases and large language models, which situates semantic retrieval within the broader workflow of retrieval-augmented generation: a semantic search module identifies relevant passages that a language model then uses to formulate a grounded response.

Applications

Semantic search has applications in a range of fields, including:

• Web search engines, where result relevance to query intent is prioritized over keyword frequency
• Enterprise knowledge management, where internal documents, tickets, and code are retrieved by meaning
• Question answering systems that select relevant passages from large document corpora
• E-commerce product discovery, where user intent must be matched across varied product descriptions
• Legal and biomedical document review, where precise conceptual retrieval across specialized vocabularies is required