What is semantic search?

Semantic search is a search engine technology that interprets the meaning of words and phrases. The results of a semantic search will return content matching the meaning of a query, as opposed to content that literally matches words in the query.

Semantic search is a set of search engine capabilities, which includes understanding words from the searcher’s intent and their search context.

This type of search is intended to improve the quality of search results by interpreting natural language more accurately and in context. Semantic search achieves this by matching search intent to semantic meaning with the help of technologies such as machine learning and artificial intelligence.

Semantic search is powered by vector search, which enables semantic search to deliver and rank content based on context relevance and intent relevance. Vector search encodes details of searchable information into fields of related terms or items, or vectors, and then compares vectors to determine which are most similar.

A vector search-enabled semantic search produces results by working at both ends of the query pipeline simultaneously: When a query is launched, the search engine transforms the query into embeddings, which are numerical representations of data and related contexts. They are stored in vectors. The kNN algorithm, or k-nearest neighbor algorithm, then matches vectors of existing documents (a semantic search concerns text) to the query vectors. The semantic search then generates results and ranks them based on conceptual relevance.

1. When a query is launched, the search engine transforms the query into embeddings, which are numerical representations of data and related contexts. They are stored in vectors.
2. The kNN algorithm, or k-nearest neighbor algorithm, then matches vectors of existing documents (a semantic search concerns text) to the query vectors.
3. The semantic search then generates results and ranks them based on conceptual relevance.

Context
In semantic search, context can refer to any additional information, such as the searcher’s geographical location, the textual context of the words in the query, or the context of the searcher’s search history.

Semantic search uses context clues to determine the meaning of a word across a dataset of millions of examples. Semantic search also identifies what other words can be used in similar contexts.

For example, a search for “football” would mean “soccer” in the USA and "football" in the UK and other parts of the world. Semantic search would distinguish results based on the user’s geographical location.

Searcher Intent
The role of semantic search is to improve the user’s experience. To provide the most relevant results, it interprets the user’s intent to understand their needs. Do they want information? Are they trying to make a purchase? Based on the query and its context, semantic search will rank results in order of relevance.

Semantic search can also be modified or improved with a query categorization setting, such as producing the highest-rated products first and the lowest-rated products further down.

Related: Personalize your users' search experience

Semantic search vs. keyword search

The difference between semantic search and keyword search is that keyword search returns results that match words to words, words to synonyms, or words to similar words. Semantic search looks to match the meaning of the words in the query. In some cases, semantic search might not generate results with direct word matches, but it will match the user’s intent.

Keyword search engines use query expansion or relaxation tools like synonyms or word omission. They also use natural language processing and understanding tools like typo tolerance, tokenization, and normalization. On the other hand, semantic search is able to return query results that match meaning through the use of vector search.

Consider "chocolate milk." A semantic search engine will distinguish between "chocolate milk" and “milk chocolate.” Though the keywords in the query are the same, the order in which they are written affects the meaning. As humans, we understand that milk chocolate refers to a variety of chocolate, whereas chocolate milk is chocolate-flavored milk.

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Semantic search is important because it facilitates a broader search surface. Since it is powered by vector search, semantic search enables a more intuitive search experience where a query's context and intent yield results.

Because semantic search algorithms continue "learning" with a variety of key performance indicators (KPIs), such as conversion rates and bounce rates, semantic search helps boost user satisfaction.

Deep dive into 2024 technical search trends. Watch this webinar to learn best practices, emerging methodologies, and how the top trends are influencing developers in 2024.

Semantic search offers results based on the user’s geographical context, the user’s past search history, and user intent.

Personalization uses the searcher’s previous searches and interactions to determine response relevance and rank. Semantic search can also rerank results based on how other users have interacted with the responses it has pulled. For example, when you type "restaurants" into your search engine, it will produce results that are in your area.

With a better understanding of user intent, semantic search can respond to a query like "Creuset vs. Staub dutch ovens" with content that prioritizes product comparisons because that is the user’s intent. Semantic search will recognize the intent behind “best Staub deals” or "Creuset discounts" as intent to purchase and offer responses accordingly.

Another example is predictive text. As you type a query into a search bar, it uses semantic search to complete your query and suggest relevant search terms based on context, common searches, and past search history.

Semantic search benefits companies and their customers by improving the search experience.

Easier to use for customers
Customers might not remember jargon, or recall specific product names. Semantic search enables customers to input vague search queries and get specific results. Customers can also search using a description to discover its name. For example, you can discover a song by searching for the lyrics that you know and find the title.

Because semantic search interprets meaning by considering intent and context, the experience on the customer side feels more like human interaction.

Concepts are more robust than keywords
By matching concepts rather than keywords, semantic search produces more accurate results. Through dimensional embeddings, a vector represents a word as a concept. "Car" is no longer only matched to "car" or "cars," it is also matched to "driver," "insurance," "tires," "electric," "hybrid," and so on because those words are connected to the vector of "car."

So, semantic search that is vector search-powered expands on the concept of simply matching keywords represented by tokens.

Better for business
By understanding user intent, semantic search can boost sales and customer satisfaction. User intent can be informational, transactional, navigational, or commercial. Understanding intent allows a search engine to better meet customer needs. This improves the customer’s relationship with the brand, which is better for business.

The Elasticsearch platform is equipped with machine learning and AI solutions, including a semantic search model: the Elastic Learned Sparse EncodeR — or ELSER. This NLP model is trained by Elastic to enable semantic search in an easy-to-deploy tool.

Watch this video and learn about Elastic's zero-shot encoder model: how it works and why it can help you implement semantic search fast for AI search apps and generative AI experiences. Elastic's machine learning model requires no training, so it saves on costs and time.

Learn more about Elasticsearch

• Semantic search excellence: Get started with AI
• Semantic search excellence: Advanced concepts with AI
• Semantic search: Bringing search experiences into the AI era
• AI search algorithms explained
• Dive into the basics of natural language processing (NLP) models and vector search with Elastic
• Watch this video to learn more about vector search and its applications
• Understand how to deploy NLP with Elastic
• Master kNN search with Elastic

Embeddings: Embeddings are vectorial representations of word meaning. They are the form a word takes when processed for a search.

Inverted index: An inverted index is a database that enables search engines to retrieve information. It matches words to numbers, or numbers to numbers, to their location in a database, document, or set of documents. A record-level index will match words to one or multiple documents. A word-level index will match words to where they are within the document.

Natural language processing (NLP): Natural language processing is a subfield of computer science, linguistics, and artificial intelligence that enables computers to understand and process human language. Normalization: Normalization refers to the process of transforming data numerical values into a common scale.

Semantics: Semantics is a branch of linguistics and logic concerned with meaning. It is also a subfield of computer science and philosophy.

Tokenization: In NLP, tokenization refers to the process of converting sentences into tokens, or smaller units of information. It is a process that enables faster computer processing.