Semantic Search

What is Semantic Search?

Semantic search is search that matches meaning, not only exact words. It focuses on contextual meaning and query intent, so the search engine can understand what the user wants.

Keyword search may match words inside a query. Semantic search can also match related concepts, entities, synonyms, and user need. That helps when someone searches waterproof shoes for monsoon walking.

What semantic search needs to work

Semantic search needs a query, an index, meaning signals, retrieval logic, and ranking logic. A working system may use query analysis, knowledge graphs, content analysis, and result retrieval.

Query analysis reads user text. Search software may identify phrases, entities, and likely intent. Content analysis reviews stored records for topics, entities, wording, and semantic similarity.

NLP terms used in semantic search

Natural language processing, or NLP, helps software process human language. Tokenization splits text into smaller parts. Entity recognition finds names, products, places, people, and concepts. Intent detection estimates the user goal behind a query.

Semantic search can use NLP before retrieval. A query such as phone with good camera for night photos contains a product, feature need, and use case. Keyword search may miss pages that use low-light photography instead of night photos.

How entities and knowledge graphs help

An entity is a named thing with one distinct meaning. Examples include Google, Mumbai, running shoes, BM25, and Sentence-BERT. A knowledge graph stores entities and relationships between them.

Entity relationships reduce ambiguity. Apple may mean fruit or Apple Inc. Query context, nearby words, page category, and entity links help search software choose the right meaning. Knowledge graphs help search systems connect entities, context, and related meaning.

How embeddings and vector search support semantic search

Embeddings turn text into number arrays. Dense vectors can represent semantic meaning, so related ideas can sit near each other in vector space.

Vector search compares a query vector with stored record vectors. MongoDB Vector Search supports retrieval based on embeddings closest to a query vector.

Sentence-BERT created sentence embeddings for semantic similarity tasks. Sentence embeddings can be compared with cosine similarity to estimate how close two sentences are in meaning.

Semantic search compared with keyword, vector, and hybrid search

Semantic search is a broad meaning-based concept. Vector search is one technical method that can support semantic search. Keyword search focuses on exact terms. Hybrid search combines lexical and vector retrieval.

Method	Matching signal	Good use	Main limit
Keyword search	Exact words	SKUs, error codes, names	Misses related wording
Vector search	Embedding closeness	Natural questions	Can miss exact terms
Hybrid search	Text and vectors	Mixed record sets	Needs tuning
Semantic ranking	Reranked candidates	Rich text records	Depends on first retrieval

Azure semantic ranker works after an initial BM25-ranked or RRF-ranked result set. It improves ordering inside that candidate set, rather than searching the full index again. See the section on reranking existing search results.

Cosine similarity formula

Cosine similarity compares vector direction. In information retrieval, query and document vectors can be scored through cosine similarity between vectors. Treat the formula as a learning model, not a universal product rule.

Symbol	Plain meaning
q	query
d	document
V(q)	query vector
V(d)	document vector
V(q) · V(d)	dot product

Example calculation:

A score of 0.8 means both vectors point in a similar direction. Real embedding vectors normally use far more than two dimensions.

Example semantic search query

Example input: durable shoes for monsoon walking.

Keyword retrieval may match durable, shoes, monsoon, and walking. Vector retrieval may also match waterproof footwear, rain shoes, walking sandals, and grip sole. Hybrid retrieval can collect exact-term matches and embedding-near matches.

Ranking still needs good source records. A product page with no sole material, waterproof detail, size data, or use case can rank poorly for shoppers. Semantic search cannot add facts that stored records do not contain.

Where semantic search works best

Semantic search works best when readers use varied wording. It helps with natural questions, synonyms, broad topics, product discovery, support searches, and knowledge-base search.

Keyword search remains better for exact identifiers. Use keyword search for legal names, model numbers, product SKUs, error codes, invoice IDs, and policy codes. Use hybrid search when natural wording and exact codes both matter.

Checks before semantic search goes live

Create a test set before launch. Include common queries, synonym queries, ambiguous queries, no-result queries, and exact-code queries. Label expected results for each query before judging output quality.

Review wrong matches after each index update. Check filters, stale records, duplicate records, missing metadata, and weak product descriptions. Hybrid search can use RRF to merge ranked results from multiple query methods.

Limits readers should know

Semantic search cannot prove that stored text is correct. Embeddings can connect related wording even when source records contain outdated facts, weak descriptions, or wrong metadata.

Semantic ranking depends on earlier retrieval. Azure semantic ranker reranks existing top results and uses stored content for captions and answers.

Human review still matters for medical, legal, finance, safety, and policy search. Search quality also needs clean records, current metadata, useful filters, and regular relevance checks.