What Exactly is Semantic Search?

As technology has advanced, so too has the way that we collect and search for information. We now live in a data-driven world and a digitized economy, with Cyber Security Ventures reporting that in 2025, the world will store more than 200 zettabytes of data, and this number is going to increase year-on-year. 

As Shashi Teja outlined in his post on Data Analytics for Beginners, data has become a progressively important tool for businesses and individuals alike as it is used to uncover patterns, trends, and insights that can inform decision-making. It is for this reason that data is commonly referred to as the new oil and will play an increasingly important role as society becomes more integrated with modern technology. As data has evolved, so too has the ability to search through it in new ways. One search method that is becoming increasingly used is the semantic search. 

How a Semantic Search is Different From a Keyword Search

The most common way to find information in a database or a search engine is by using a keyword search. This would involve using specific words to find information within a database or search engine. These keywords would represent the core of the search query to find the desired result. Keyword searches are a valuable tool for finding information, especially when the user knows what they’re looking for. A semantic search is different from a regular keyword search in that it doesn’t look for an exact match for the inputted query. Getting Started With Semantic Search, a guide on Medium, explains how this method of searching has an understanding of natural language and identifies results that have the same meaning, not necessarily the same keywords.

Compared to a keyword search, a semantic search provides more accurate and relevant results for ambiguous or complex queries. For example, if you were to search for running shoes, a semantic search would return all results that are associated with the query, such as sneakers and other types of athletic footwear. If the semantic search is attached to a database with consumer information, the search would find related running products based on their previous purchase history.  

What Databases and Search Engines Use a Semantic Search

Modern data management systems are increasingly providing semantic search features as companies use data to improve every level of their operations. Vector databases are the top databases for performing a semantic search because the way they store and organize data is optimized for this type of search. A MongoDB overview of vector databases outlines how information is stored on a vector, which is usually represented as a list of numbers. Each number in the list represents a specific feature or attribute of that data. The vector is then stored in a multi-dimensional space in the vector database, where it naturally clusters with other vectors that have a similar contextual relationship. This indexing technique enables much faster searching across massive data sets using a semantic search.  

Semantic searches have also become increasingly common in search engines, with both Google and Bing offering this search function. Google’s semantic search uses AI and natural language processing (NLP) to understand the meaning behind search queries, providing more relevant results than traditional keyword-based search. This allows Google to provide results that align with the user’s intent, even if it means finding results that don’t exactly match the search query.

Real World Applications of a Semantic Search

Semantic searches are being used across different industries, including ecommerce, healthcare, and customer support, as well as in the training of AI applications. In ecommerce, the platform will use semantic searches to provide recommendations to customers based on their search queries, browsing history, and previous purchases. This allows the ecommerce platforms to offer a much more personalized experience. The healthcare industry is increasingly using semantic searches to go through vast amounts of medical literature and patient data to provide medical professionals with information that is most relevant to specific cases. Automated Customer Support applications, such as chatbots, are now the heart of customer service. This is because they can leverage databases with semantic searches to train NPLs to understand and respond to customer queries, providing timely and accurate assistance.

Semantic searches are increasingly becoming the dominant way we find information as our ability to use and understand data has evolved.

Also Read: Step-by-Step Guide to Implementing Salesforce Experience Cloud for Your Business