Sometimes using a search engine feels like a brute-force password attack: you keep trying search phrases until you find the one that works. Everyone’s encountered a website search bar that doesn’t seem to understand anything except exact-match search phrases.

If you don’t use an exact match, you’re sent to a page that looks like this: 

This is one of the troubles with lexical search. A search engine that uses lexical search doesn’t consider the meaning, intent, or context of the person’s query. 

It only looks for literal query matches. 

In this guide, we define semantic search, how it differs from other search techniques (and why you might want more than one search technique), and the technology that makes it possible. We’ll also review what you should consider when implementing semantic search for your own organization.

Semantic search is a sophisticated information retrieval technique that goes beyond traditional lexical (keyword-based) search. It matches the content of a query to documents based on their meaning and user intent, rather than just focusing on specific words or phrases. 

This approach helps address vocabulary gaps, ambiguity, polysemy, and complex queries. By connecting a user’s search context with the relationships between words, semantic search engines can surface results that are much more relevant to an individual user during their search.

In other words: 

Semantic search opens the door to natural language search experiences; it expands the recall ability of a search engine. Traditional keyword search, on the other hand, is focused on precision — that is, retrieving exactly what was asked for, regardless of additional intent or context.

The Coveo AI-Relevance™ Platform achieves its unique flavor of relevancy by combining both semantic and lexical search — learn more about hybrid search. 

Lexical search and semantic search take fundamentally different approaches to retrieving information. 

Lexical search relies on exact keyword matching, meaning it only retrieves results that contain the exact words in the query. This approach struggles with ambiguity, fails to recognize synonyms, and does not account for user intent. 

As a result, if a user searches with slightly different phrasing than what’s in the indexed content, they may receive irrelevant results or none at all. Additionally, lexical search does not personalize results, as it does not analyze user behavior, preferences, or previous searches.

Semantic search, on the other hand, is designed to understand meaning, context, and intent. By leveraging AI-driven techniques like natural language processing (NLP) and machine learning, semantic search can recognize synonyms, disambiguate queries, and tailor results based on a user’s past interactions. 

This makes it particularly effective for handling complex queries, delivering personalized search experiences, and presenting more relevant content through techniques like concept mapping and entity recognition. 

While lexical search ranks results based primarily on keyword frequency, semantic search can establish relationships between different pieces of content, ultimately improving the user experience with richer and more intuitive search results.

Why Is Semantic Search Important?

You build search experiences to connect people with what they’re looking for. That’s what people want too: the quickest, most intuitive path to the answers they seek. Semantic search helps to remove friction from the search experience, while improving the relevance of search results across the board.

It does so by considering a lot of different data points, usually with the help of AI.

Consider this example; if you were to search: “How does vector search improve relevance in modern search engines?”

Lexical search would return documents containing the exact keywords “vector search,” “relevance,” and “search engines,” but may miss the actual explanations or mechanisms behind the technology. 

Semantic search, however, understands that the query seeks a conceptual understanding of how embedding-based techniques like vector representations and nearest neighbor algorithms (e.g., ANN, FAISS, HNSW) enhance semantic relevance, ranking, and retrieval quality in modern AI-powered search systems. It retrieves content that discusses concepts, even if the exact query terms aren’t used.

Think about how important this capability is when someone tries to troubleshoot a problem on their own; they may not know how to exactly describe the problem or need they have. Or when they go shopping online; a shopper may have a vague idea of what they’re looking for, but that’s leagues away from actual product names or descrptions. A recent Coveo case study revealed that an enterprise retailer doubled their  conversion rate, thanks in part to the power of semantic search capabilities and AI. 

When building search experiences for customer support and ecommerce — for healthcare, finance, and your own internal teams — context is huge. Combining this recall with the precision of lexical search means you get the best of both worlds; and so do your searchers, be they employee or customer. 

How Does Semantic Search Work?

Most people using search engines don’t know much about semantic search. Its accuracy and relevance can feel like magic. Under the hood, there are a few important technical components that make this magic happen: 

Embedding Models

An embedding model is an ML model that helps capture semantic meaning within data. It works by transforming data (words, sentences, and images, for example) into vectors that feed into machine learning tasks. You can use embedding models in information retrieval, recommendation systems, and NLP. 

Examples of Embedding Models

  • Word embeddings (FastText, Word2Vec)
  • Sentence and document embeddings (Bidirectional Encoder Representations from Transformers)
  • Image embeddings (Convolutional Neural Networks
  • Graph embeddings

Natural Language Processing (NLP) & Natural Language Understanding (NLU)

Natural Language Processing (NLP), a specialized area within artificial intelligence (AI), transcends basic keyword matching by leveraging natural language understanding (NLU) to discern the semantic meaning of a search query. Its goal is to deliver the most relevant results by interpreting the context and intent behind a search.

Unlike search engines that are solely keyword-based, NLP search engines can grasp the intricacies and nuances of human language. On the other hand, this also means it can struggle with broad or general search queries. 

NLP search engines have a few other distinguishing features: 

  • They rely on query permutations to deliver conceptual search results
  • They iterate and learn as you feed then more information
  • They benefit from semantic similarity matching, a sub-task of NLU
  • They continuously improve performance thanks to deep learning 

Machine Learning (ML)

Another component of AI, ML improves algorithmic performance as we introduce more data to the system. NLP search engines use ML to continuously improve results based on user interactions, content engagement, and other variables.

Coveo offers machine learning that leverages user analytics to predict and recommend which content is most helpful to users. This machine learning works on and learns from user behavior analytics, reviewing what users are doing on interfaces like: 

  • Customer community
  • Agent support page
  • Website

It also looks at what the users are clicking on or, in the case of online purchasing, what they are following through on. In other words, when users are trying to find a solution to a problem, what steps are they taking?

Coveo provides a robust suite of machine learning models, giving enterprises the power to embed AI throughout their digital experience. 

Relevant reading: Generative AI and Top AI Models for CX

The primary advantage of semantic search is how it improves the search experience. 

  • Easier: People can search in their natural language and still find relevant results
  • Intuitive: Capable of understanding context and intent
  • Personalized: Adapts and optimizes based on user input
  • Comprehensive: Includes synonyms and other variations

When it comes to handling complex queries, semantic search has a clear advantage over traditional keyword search. Take the ecommerce use case, for example. Semantic search is well suited for ecommerce applications due to its ability to overcome vocabulary gaps, find related products based on intent recognition, and handle broad or ambiguous queries. But it’s not without its limitations.

  • Computational requirements: Requires a lot of computational power and memory to maintain, leading to higher operational costs and potential scalability issues. 
  • Short-query limitations: In contexts where users tend to use one- or two-word search phrases, semantic search has less context and intent to extract. The relevance of search results can suffer as a result. Hence why it can be beneficial to combine semantic and lexical search techniques. 
  • Bias: Semantic search engines can be prone to inherent biases that lead to unfair or inaccurate search results. 

Writing for diginomica, George Lawton makes an important distinction between Generative AI understanding vs. semantic search:

“It’s important to remember that there are many different kinds of semantic search; gen AI takes it to a more nuanced level thanks to its billions or trillions of parameters. Semantics does not just have to be about the meaning of words and sentences but also the interconnection of meaning found in audio, video, and even enterprise documents. Academics and technicians widely use this more nuanced definition, but it has fallen out of vogue in mainstream conversations.” 

Semantic Search Is One Tool in the Search Toolbox

You want your search experience to be as intuitive and natural as possible. You want people to be able to search in a conversational way, without sacrificing accuracy, relevancy, and usability. 

At the same time, you understand that there are more ways than one to build a search engine experience. Context is everything: semantic search makes a lot of sense for ecommerce use cases. How you use semantic search for an internal search engine, or for a streaming service, may vary. As with any search technology, semantic search is not without its limitations. 

Key Takeaways:

  • Semantic search improves search engine results by interpreting the context and meaning behind queries
  • Semantic search does a lot — combining it with lexical search can take those capabilities even further
  • Semantic search relies on NLP, NLU, Knowledge Graphs, and other technology

Experience the difference in search accuracy and user satisfaction with Coveo solutions. Learn more about Coveo AI Site Search and Content Discovery.