What Is Semantic Search? How It Works, Benefits & Examples

Quick Overview:

• Semantic search focuses on the meaning, context and intent behind any query and not just looks for the exact words. It can even find results if the words are not the same.

• It uses embeddings, vector databases, similarity search, reranking and other technologies to turn queries and documents into representations that are compared to see if they are relevant.

• It is used in e-commerce, enterprise search, customer support, healthcare, and RAG applications.

• In this blog you will learn about semantic search in detail along with its benefits working, tools and technologies used and real world applications.

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What Is Semantic Search?

Semantic search is a search method that tries to understand what a user or query actually means, not only the words it contains. It does not only match the text string like a keyword, but it looks at the intent, context and the relationship between concepts.

When searching for "affordable laptop for coding" semantic search will return results about the programming laptops which are budget friendly, even none of the page uses the word “cheap”.

That's the semantic search meaning in one line: it ranks results by what they mean, not by word overlap.

Let’s understand it more clearly with the help of a semantic search example:

Example of Semantic Search

Imagine you are browsing an online shopping website and searches for:

“comfortable shoes for long walks” 

The result the page shows, the product description may not contain the exact phrase “comfortable shoes for long walks”. But, the product description may contain terms such as “cushioned sole”, “walking shoes”, “arch support”, and “all-day comfort”.

Semantic search can connect these related concepts and return products that match your intent.

Now let’s explore what are the benefits of semantic search.

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What Are the Benefits of Semantic Search?

Semantic search can improve the quality of search results in several ways. Below are some of its key benefits:

Understanding these key benefits make your key idea of semantic search clear. Now let’s look at some of the key components of it to understand it more clearly.

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Key Components of Semantic Search

Below are the components that work together to understand the queries, information representation , retrieve relevant results, and rank them.

If you are confused with the components discussed above, let’s discuss how they all work together to understand the semantic search more effectively.

How Does Semantic Search Work?

The process of how semantic search works can be explained through a simple flow. Below are the key steps involved in the working of semantic search:

1. A user enters a query

When a search query is submitted by a user, the process begins.

For example: “Best ways to learn Python for data analysis”

After sending the query to the search system, the latter will do the pre-processing to make it ready for computation.

2. The query is converted into an embedding

A type of language model called the embedding generator takes the query text as input and creates an array of numbers that represents the meaning of the text.

The embedding of a word or phrase contains the semantic features of the word or phrase. Two meanings which are more alike will tend to generate vectors which are closer in the embedding layer.

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3. Documents are converted into embeddings

The texts that have to be searched will also be converted into vector format.

In many cases, a knowledge base with tutorials, articles, product descriptions, or company documents will also have thousands to millions of embedded records.

4. The system compares the vectors

Following the translation of the query and the content into their vectors respectively, the matching of the query’s vectors with the ones in the database will take place.

The most relevant documents/text fragments related to the input query can be matched using techniques such as cosine similarity.

5. Relevant results are retrieved

The most closely matching documents or passages are displayed.

As an example, a query for "learn Python for data analysis" could result in returning content on Pandas, NumPy, data cleaning, and Python data analysis courses, even though their wordings are not exactly similar.

6. Results may be reranked

Post initial retrieval stage, some search platforms use a reranking algorithm.

Reranker examines all the results which have been recovered, and then ranks the most relevant ones highest in the overall results display.

7. Results are shown to the user

Presentation of end results depends on their relevance levels.

At times, when the information that users need is available through text retrieval engines, a language model can be supplied with that content to produce a natural language response based on the retrieved ‍‍‍document.

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Key Technologies Powering Semantic Search

There are several technologies that work together to make semantic search possible which includes NLP, Vector Search, Reranking Models etc. Let’s explore each of these in detail:

1. Embeddings

Embeddings convert text into numerical vectors that capture meaning and relationships between pieces of content.

For example, “car”, “automobile”, and “vehicle” may have embeddings that are relatively close because their meanings are related.

2. Natural Language Processing

NLP assists in dealing with natural language for computers. Various operations like tokenization, text cleansing, language detection, and query understanding could be involved in the larger search process.

3. Vector Search

Vector search compares numerical representations to find similar content. It is one of the main technical methods used to build semantic search systems.

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4. Vector Databases

Vector databases store embeddings and support similarity searches across large datasets.

They are commonly used when semantic retrieval needs to operate over large document collections.

5. Reranking Models

Initial vector retrieval may return several potentially relevant results. A reranking model can examine these results more closely and improve their order.

5. Hybrid Search

Hybrid search combines semantic retrieval with traditional keyword-based search.

This is useful when exact terms matter as well as meaning. For example, a product code, technical term, or person's name may require exact matching, while the rest of the query benefits from semantic understanding.

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Tools Used for Semantic Search

Different tools can be used depending on the size and complexity of the search application.

Now let’s understand where is semantic search is used in real world.

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Applications of Semantic Search in Real-World

Semantic search is used in different sectors for different works such as e commerce, customer support etc. Let’s understand these use cases in detail:

1. E-commerce

Online stores can use semantic search to understand product intent.

A query such as “lightweight shoes for running” can return products described as “breathable”, “low-weight”, or “performance running footwear”.

2. Enterprise Search

Businesses often store their data in documents, wikis, emails, policies as well as internal systems. This information may be spread out.

One of the main advantages of using semantic search techniques is helping people locate important data even in case an employee's query word-by-word is not the same as an internal paper one would expect.

3. Customer Support

Support systems can match user questions with relevant knowledge-base articles.

For example, “my account is locked” may match an article titled “How to reset a blocked login”.

4. RAG Applications

In terms of Retrieval-Augmented Generation, you can say that semantic search plays a crucial role in this process.

For example, if someone is questioning, then the machine will look for the relevant documents in the library of the knowledge base to give that language mode extra clues so that it will be able to generate the most useful outputs, with support from that piece of information.

5. Healthcare Search

In the case of healthcare, by linking semantic queries, patient medical records can be used to obtain relevant medical documents and other related material.

In any such application, it's very important that data protection and validation are carried out since wrong retrieval can lead to severe harm.

While discussing semantic search, it is important to also understand the difference between Semantic Search vs Vector Search. So now let’s explore this difference in detail.

Semantic Search vs Vector Search

Semantic search describes the goal of understanding meaning and returning relevant results. Vector search refers to a technical retrieval method that compares vector representations.

Let’s understand the difference between both with a help of quick table:

From the discussion above, now you are able to understand that a semantic search looks beyond the words in the query and understands the meaning of a query. It can use embeddings, vector databases, similarity search, and reranking to bring up more relevant information.

There has been a transition from a simple web search to the various enterprise knowledge systems, e-commerce applications, customer service platforms, AI agents, and RAG scenarios as the applications of semantic retrieval.