⚡ Quick Answer

Semantic search is a search technique that understands the meaning, context, and intent behind a query instead of relying only on exact keyword matches. It uses technologies such as natural language processing, machine learning, knowledge representation, and vector-based methods to identify and return information that is relevant to what the user actually means.

What Is Semantic Search?

Semantic search refers to the way information is retrieved from search queries. Traditionally, search systems attempt to match keywords within search queries to the same keywords that have been previously indexed. However, semantic search can examine the inter-relationships between words and phrases and even entire entities in search queries in addition to the overall context in which words are used.

By understanding what a person is searching for, semantic search can deliver results even when the exact words and terms have not been used. A search containing synonyms, terms that have different meanings, natural language, or concepts expressed in different ways can all be handled by semantic search.

For instance, someone who looks for “the best phones for photography” is not looking for websites that contain these exact words. What this person is looking for are the best phones that have great cameras, good image processing, optical stabilization, etc. Semantic search tries to understand the user’s intent and returns the most relevant results.

As such, semantic search is used in a wide range of applications from simple websites to enterprise knowledge management systems, e-commerce sites, document management systems and many more. It is used in applications that are powered by AI.

How Does Semantic Search Work?

Semantic search systems utilize a host of AI-related information-retrieval techniques, including natural language processing (NLP) to process search queries, and machine learning to identify and extract relevant information from large amounts of data. Knowledge representation methods such as knowledge graphs are also used to improve the search relevance of semantic search systems. Vector-based representations of documents and queries are another means by which semantic search systems can measure semantic similarity between search queries and relevant documents.

Understanding the Search Query

First the search query is analyzed. The search words and phrases are analyzed to identify the entities in the search query. The relationships between the entities in the search query are analyzed. The intent of the search query is analyzed.

Understanding complex search queries is vital for conversational search. Complex search queries are those where the user has not used the same terminology in their search query as has been used in the content that they are searching for. The semantic search algorithm therefore needs to be able to interpret the underlying meaning of search queries and content.

Understanding the Meaning of Content

When performing a search the content needs to be analyzed for topics, entities, relationships, and semantic characteristics. When a query is entered the search results are matched up against the analyzed content to find the best match.

This helps connect the search query with relevant content even when the content uses slightly different terminology to discuss the same concept.

Finding Relevant Results

Based on the search query and the available content, semantic search ranking algorithms can identify the most relevant results for any given user information need. Use of semantic similarity, entities, context, and other ranking signals enable search results to be more relevant than ever before.

In addition to ranking based on keywords, semantic search enables ranking based on a wide array of signals, providing more relevant search results.

Using AI, NLP, and Machine Learning

Artificial Intelligence: This is the technology that makes semantic search possible. Natural Language Processing or NLP can process human language to interpret information and find answers, whereas Machine Learning can process large datasets to identify patterns, relationships, and even unknown unknowns.

For semantic search, modern search systems employ so-called embeddings or vector representations of text, i.e., the search query is converted into a number vector, which can then be compared with the number vectors of the stored content.

Knowledge graphs are another way to store entities and their relations. The semantic search system then can use this knowledge to even better understand the content as well as the relations around it.

Semantic search results in improved relevance, as the system processes the search query in the same way that it processes content and thus is able to return the best possible results, even if the search query was worded differently from the content.

It helps users who don’t know the exact terms used to describe information on a page or in a product listing, to find the information they need for very complex or ambiguous search queries.

In addition to more relevant results, semantic search also improves the search experience overall. When search results are relevant, users find what they need faster and are less likely to rephrase a search query or review lots of irrelevant results.

In addition, it is possible to develop search engines that better understand natural language-based queries such as questions or even long sentences. Google Cloud highlights relevance, user experience and engagement as major benefits of semantic search.

What Are the Applications of Semantic Search?

Semantic search is a technique that can be used in many situations where users search for information online and want to find the most relevant results.

E-commerce Search

In an e-commerce application, semantic search is used for product search. For example, a customer might search for “warm clothes for winter travel” whereas the description of corresponding products would contain terms such as “insulated jackets”, “fleece clothing”, “thermal wear” or “weather-resistant apparel”.

Semantic search can identify relationships between different search terms, so search terms like “warm clothes for winter travel” can be matched with products that contain words like “insulated”, “jacket”, “fleece”, “thermal” or “weather resistant”.

Enterprise Search

As organizations create more documents to support their employees, these documents can become hard to search using conventional keyword searching. Such documents may include internal policies, reports, and even knowledge-base articles.

Employees can use natural-language queries to find information based on the meaning of their search. This makes search easier to use for employees to locate internal information within large repositories of knowledge.

Document Search

Semantic search helps users find relevant parts of documents even if the search terms used are different from the terms used in the document.

The other areas where semantic search is useful are for searching within research databases, legal documents, technical documents, and in general any other type of large collection of business content such as educational resources and information.

Conversational Search

Conversational search – search using natural language (complete questions) rather than short keyword lists, such as that found in search engines.

Semantic search can also be very useful for conversational search. It allows the user’s questions to be interpreted well and information to be selected that corresponds to the underlying request of the question. This is particularly important for so-called AI assistants and question answering systems.

Knowledge Retrieval

The retrieval of information from structured as well as unstructured knowledge sources is another area where Semantic search is very useful. The search for information in such cases is connected with the relevant information based on concepts and relationships in the query rather than only exact text.

This makes it an important component for many knowledge and retrieval systems that are powered by AI.

Semantic Search vs. Vector Search

Semantic search and vector search are closely related, but they are not exactly the same thing. 

Semantic search is the broader approach of understanding the meaning and intent of a query to retrieve relevant information. It can use several techniques, including natural language processing, knowledge representation, machine learning, knowledge graphs, and vector representations. 

Vector search is a search technique used in search systems. As mentioned previously, vector search can represent documents in a vector space. For search queries, we can also represent them in a vector space. Then we can calculate the similarity of a query with documents in the vector space.

Using vector search as part of a semantic search system is one of the ways that the larger semantics goal can be met. But semantics search can also incorporate other information, other sources, and even other signals.

Knowing the difference between Vector Search and Semantic Search will be key to design effective Search Interfaces when building AI Search Applications. Vector Search is a retrieval technique, whereas Semantic Search is a broader goal that Vector Search can help to achieve.

Real-World Examples of Semantic Search

The ability to connect user queries with relevant information is one of the main areas in which semantic search is applied.

An example of the successful application of Semantic Search to e-commerce search could be a user looking for “comfortable shoes for long walks”. As opposed to a simple keyword search that would return results containing the exact phrase “comfortable shoes for long walks”, the user is presented with products like walking shoes, cushioned footwear and even supportive sneakers.

In an enterprise knowledge management system, employees can search using normal language and retrieve all relevant documents such as policies, reports etc. Even if documents are written using formal language, semantic search is able to identify related concepts.

This can be seen in content discovery, for instance when articles, videos or other media are searched for based on similar subject matter. They don’t need to contain the exact same keywords.

Semantic search is used in many search applications such as AI-powered question answering systems to first search for relevant parts of a knowledge base and then use an AI model to generate answers from those retrieved parts.

An example of natural-language search is “What is the weather expected to be in Paris next week?” This question conveys several important pieces of information about what the user is looking for. A good semantic search system can identify the location, time frame and intent of the query rather than treating it as so many unrelated words.

Frequently Asked Questions

Semantic search is a way of finding information based on what a search query means rather than only matching the exact words used in the query. 

Using natural language processing, machine learning, knowledge representation, knowledge graphs and vector space representations to model the relationships in queries and documents to search them.

Keyword search can look for exact words, phrases or even word sequences. But semantic search takes it to the next level, focusing on the meaning, intent and even the relationships between the query and the content being searched for.

This is different from vector search as Semantic Search is the larger concept of searching for information based on meaning, whereas Vector Search is one of the many search methods that can be used for Semantic Search.

AI applications often need to look up information by meaning rather than by exact word. Therefore, semantic search supports the connection of user input with corresponding documents, knowledge, products or other information.

The use of semantic search is widespread in areas such as e-commerce, search in enterprises, document search, knowledge systems, conversational search, recommendations, and many applications using AI for question answering.

Yes. The ability to understand natural-language questions is a key application of semantic search. This allows a user to ask a complete question and retrieve relevant documents, information, products etc. rather than having to enter keywords only.

Modern semantic search applications, including search for natural language questions, often use embeddings to map queries and content to vectors, that can be compared to measure similarity to semantically related information.