Semantic search is a search approach that attempts to understand the meaning, concepts, entities, relationships, and intent behind a query, rather than matching results only according to the exact words a person types. It represents an important evolution from older lexical search, where matching words and phrases played a much larger role in deciding which documents were relevant.
Google itself has described early Search as relying heavily on matching words in a query with words on webpages. Over time, systems such as the Knowledge Graph, RankBrain, neural matching, and BERT made it possible to understand queries and documents at a deeper conceptual level.
A simple example illustrates the difference.
Query: “hot dog stand near Central Park”
Older Lexical Search based on Keyword Matching: A keyword-focused retrieval system would place considerable importance on pages containing words such as hot dog, stand, and Central Park. A highly relevant page that instead described a “frankfurter kiosk next to Manhattan’s main park” could be harder to retrieve because its wording differs substantially from the query.
Modern Semantic Search based on Meaning: A semantic search system can recognize that hot dog is a type of food, stand may refer to a vendor or kiosk, and Central Park is the famous public park in Manhattan, New York City.
The semantic search system can therefore connect conceptually related queries and documents even when their vocabulary is not identical.
| Aspect | Lexical Search | Semantic Search |
|---|---|---|
| Primary Match | Words and character strings | Meanings and concepts |
| Exact Keywords | More important | Useful but not always required |
| Synonyms | May require explicit matching or expansion | Can be understood contextually |
| Entities | Limited understanding | Entities and relationships can be recognized |
| Conversational Queries | More difficult | Better suited to natural language |
| Ambiguity | Harder to resolve | Context can help determine intended meaning |
| Retrieval | Primarily term-based | Can include neural, entity, and vector-based retrieval |
Modern search engines are not purely lexical or purely semantic. Traditional keyword matching continues to operate alongside semantic, neural, entity-based, and other retrieval and ranking systems.
How Search Engines Understand Meaning
Semantic search does not depend on one algorithm. Search systems can combine natural language processing, entity understanding, knowledge graphs, neural models, embeddings, and vector-based similarity to interpret queries and find relevant information.
Natural Language Processing and Context
Natural language processing (NLP) helps machines analyze human language, including words, sentence structure, context, and relationships between terms.
Meaning often depends on surrounding words. For example, bank could refer to a financial institution or the side of a river. A semantic system considers the wider query to determine which meaning is more likely.
Google’s BERT system was an important advancement in this area because it can examine words in relation to the other words surrounding them. Google says BERT helps Search understand how combinations of words express different meanings and intents by considering the context in which those words appear.
Entities and Knowledge Graphs
An entity is a distinct person, place, organization, product, concept, or other identifiable thing.
Instead of treating Central Park simply as two words, a search engine can recognize it as a particular geographical entity and connect it with attributes and related entities: Central Park → Park → Manhattan → New York City
Google introduced the Knowledge Graph to build this kind of understanding around real-world entities and their relationships, describing the shift as moving from matching strings toward understanding things.
Embeddings
An embedding is a numerical representation of data designed to capture useful characteristics and relationships in that data. Words, queries, sentences, documents, images, and other items can be represented as numerical vectors.
Consider:
- cheap running shoes
- affordable footwear for runners
The phrases use different vocabulary but convey closely related meanings. A semantic model can create representations that reflect this conceptual similarity.
Embedding Space
Embedding vectors exist within a multidimensional embedding space. In a well-trained semantic embedding space, items with similar meanings tend to occupy nearby regions, while unrelated concepts usually appear farther apart.
A useful analogy is a conceptual map: running shoes ↔ trainers ↔ athletic footwear
Here, these three terms would occupy a similar region, while mortgage calculator would be much farther away. This makes it possible to retrieve information based on semantic similarity rather than requiring exact word matches.
Cosine Similarity
One common way to compare embedding vectors is cosine similarity, which measures the angle between two vectors.
Its general formula is:
Vectors pointing in similar directions receive higher similarity scores, while vectors pointing in substantially different directions are less similar. Google lists cosine similarity, dot product, and Euclidean distance among methods that can be used to compare vector embeddings.
This does not mean Google Search necessarily uses this exact calculation for every semantic-search process. Rather, cosine similarity provides a useful model for understanding how vector systems can mathematically compare meaning.
Vector Search and Semantic Retrieval
In a vector-search system, the process can be simplified as:
This allows a query to retrieve documents whose meanings are similar even when their wording is different.
Google Systems That Contribute to Semantic Understanding
Google Search uses multiple systems that contribute to understanding concepts, language, context, and relationships.
| Google System | Contribution to Semantic Understanding |
|---|---|
| Knowledge Graph | Connects real-world entities, attributes, and relationships |
| RankBrain | Relates words to real-world concepts and assists ranking |
| Neural Matching | Matches broader concepts represented in queries and webpages |
| BERT | Interprets contextual meaning, word relationships, and intent |
| MUM | Extends information understanding across languages and modalities in specialized applications |
RankBrain
Introduced in 2015, RankBrain was Google’s first deep-learning system deployed in Search. Google says it helps Search understand how words relate to real-world concepts, allowing relevant results to be identified even when the query uses different terminology from the page.
Neural Matching
Introduced in 2018, neural matching helps Google understand broader or “fuzzier” representations of concepts in queries and webpages. Rather than considering only individual keywords, it examines the broader concepts represented by the query and content.
Google describes neural matching as an important retrieval system, helping Search locate potentially relevant documents before they are ranked.
BERT
BERT helps interpret combinations of words, their order, surrounding context, and the intent they express. This is particularly useful for conversational searches in which small words can substantially change meaning. BERT contributes to both retrieval and ranking in Google Search.
MUM
Google’s Multitask Unified Model (MUM) can understand and generate language and process information across different languages and content types, such as text and images Google has used it for specialized Search applications, but its ranking-systems documentation states that MUM is not a general ranking system in the same way as RankBrain or BERT.
Common Examples of Semantic Search
1. Deciphering Ambiguity
Consider: “Apple launch dates”
The word apple is ambiguous. However, the accompanying phrase launch dates strongly suggests Apple Inc. and technology-product releases rather than fruit harvesting.
Semantic interpretation uses the complete context rather than assigning one fixed meaning to an isolated word.
2. Understanding Conversational Queries
Consider: “How tall is the building in Paris with the lights?”
The query does not explicitly mention the Eiffel Tower, yet a semantic system can connect Paris, building, lights, and height with that entity and return relevant information.
3. Understanding Relationships Between Words
Google has used the query “can you get medicine for someone pharmacy” to demonstrate BERT. The word for changes the meaning of the query by showing that the searcher wants to know whether they can collect medicine for someone else, rather than simply collect prescribed medicine for themselves.
What Semantic Search Means for SEO
Semantic search is central to modern SEO because search engines can evaluate subjects and intent beyond exact keyword matching.
- Write for Topics Naturally: Comprehensive explanations naturally introduce related terminology, concepts, and entities.
- Avoid Keyword Stuffing: Repeating the same phrase is unnecessary for demonstrating what a page is about.
- Develop Topical Depth: Related pages can explore different aspects of a broader subject.
- Use Natural Contextual Language: Synonyms and related terminology should arise naturally from explaining the topic.
- Match Search Intent: Content should satisfy whether the user primarily wants information, navigation, commercial research, or a transaction.
- Create Clear Relationships Between Pages: Logical architecture, headings, internal links, and descriptive anchor text can make relationships easier for users and search engines to understand.
Semantic Search and Keywords
Semantic search does not mean keywords have stopped mattering. Words still provide essential information about topics, entities, and user needs. The difference is that modern search systems can interpret those words within a broader context instead of requiring pages to repeat the exact wording of a query.
Understanding search intent therefore remains important because semantic relevance is ultimately useful only when the resulting content satisfies what the searcher wants.
Semantic Search in the AI Era
Semantic retrieval has become even more important as search interfaces incorporate generative AI and increasingly conversational queries.
Neural retrieval can locate information according to conceptual similarity, while hybrid search combines traditional lexical matching with semantic or vector retrieval. Candidate documents can then undergo reranking, where another system evaluates their relevance more closely.
A simplified modern search workflow can look like:
Semantic search and AI search are closely related but are not identical. Semantic search primarily concerns finding information based on meaning, while modern AI search can build on that retrieval process by using generative models to summarize, combine, reason over, or present the retrieved information.
Frequently Asked Questions
What is semantic search in simple terms?
Semantic search attempts to understand what a query means, rather than looking only for webpages containing the same words.
What is the difference between semantic search and lexical search?
Lexical search primarily matches words and strings, while semantic search can consider concepts, entities, contextual relationships, and intent. Modern search systems commonly combine both approaches.
What is an embedding in semantic search?
An embedding is a numerical representation of information such as a query, sentence, or document. It allows systems to compare items mathematically according to their semantic relationships.
Does semantic search mean keywords no longer matter?
No. Keywords still help identify topics and user needs, but modern search systems can understand their context and relationships rather than relying only on exact-match repetition.
What is the difference between semantic search and vector search?
Semantic search describes the broader goal of retrieving information according to meaning. Vector search is one technical method that can support that goal by representing queries and documents as vectors and comparing their mathematical similarity.
