Semantic Search Explained: How It Works, and Why It Matters

It’s hard to get a good answer when your query isn’t understood. That’s long been a challenge with traditional keyword search. But now, artificial intelligence gives users the ability to ask queries in natural language, and get relevant, informative results. Semantic search uses technologies like natural language processing (NLP) to understand the intent behind a query and deliver helpful answers—fast.

Let’s explore how semantic search works and why it matters.

Defining semantic search

Semantic search is unique in that it doesn’t just focus on keywords. Unlike traditional search, which looks for exact word matches, semantic search reads intent to get a deeper understanding of the query.

For instance, if a user searches, “Where’s the updated Q2 roadmap?”, a keyword search might only find that exact phrase. Semantic search understands that the idea could be expressed differently:

• “Revised Q2 planning doc”
• “New roadmap for April–June”
• “Latest product roadmap”

Semantic search delivers the results you’re looking for even when your query isn’t an exact match.

Consider another example: “What did Amanda say about finding a new cloud provider?” Keyword search requires the exact phrase. Semantic search can find messages where Amanda mentions this idea with different phrasing, such as:

• “I’m not sure our cloud provider is the best fit.”
• “Let’s consider other cloud platform options.”

 

How semantic search works

Semantic search relies on vector search, which allows a search engine to analyze large, unstructured datasets for meaning and context. It combines AI-powered tools such as NLP, machine learning, and contextual understanding to deliver better results. Let’s take a closer look:

• NLP: This AI technology helps computers interpret human language to gain deeper understanding of a query and give natural, human-like responses. NLP pulls ideas, context, and intent from queries, allowing search engines to provide more helpful, context-specific results. NLP is key to the success of AI agents because it enables them to conduct more natural conversations with people for more effective knowledge sharing.

• Machine learning: Machine learning (ML) algorithms identify patterns, learn from data, and make predictions and decisions. ML models are trainable, improving over time with exposure to more examples. Two types of ML algorithms relevant to semantic search are: 
  • K-Nearest Neighbors (kNN)
    The kNN algorithm compares similar examples in its training data to find commonalities and identify the closest match.
  • Transformers (BERT, GPT)
    Models like BERT and GPT process entire sentences to understand relationships between words and phrases.

• Contextual analysis: Contextual analysis allows a search engine to offer personalized results based on contextual details such as user behavior, location, time, and device.

 

Benefits of semantic search

Semantic search offers a more intelligent way to find information, which makes it feel less like extracting data and more like a human conversation. Let’s look at some of the key benefits:

• Improved search results: Semantic search helps users find relevant information faster, even without precise keywords. Unlike traditional search, it understands synonyms, intent, and context, linking concepts rather than just keywords. This helps users spend less time hunting for what they need.

• Better user experience: Trying to come up with the right words and phrases to make a traditional search engine find what you’re looking for can be frustrating. Semantic search provides better results through a natural, intuitive process, making it more enjoyable for users. Quick and consistent information access also helps increase user engagement.

• Easier collaboration: Semantic search simplifies navigating knowledge bases such as help centers or resource libraries, which makes it easier for teams to work together. For example, Slack’s enterprise search, powered by AI, uses semantic search to let users search across unstructured datasets without the need for exact phrasing.

 

How to use semantic search: best practices

Semantic search is more natural and intuitive than traditional keyword search. To use it effectively, though, it helps to understand how it works and apply these best practices.

• Continuous learning: Because it relies on machine learning, semantic search works best when it has access to the most up-to-date and complete information. Feed it new data regularly to help it learn and adapt as things change in your organization.
• User feedback: Find ways to collect and implement feedback from your team members. For example, which results did a user click and which did they ignore? Direct feedback, such as asking for a thumbs-up or thumbs-down on search results, can help the system sharpen and improve.

• Clear labels and tags: While semantic search looks deeper than keywords, adding metadata to your content helps the system understand and organize it. This could include adding labels, tags, or categories to make search results more accurate.

 

Challenges and considerations for semantic search

Semantic search offers many benefits, but organizations should also be aware of potential challenges. Here are key issues to consider when adopting it:

• Data privacy: Semantic search collects and evaluates details such as user location, behavior, and clicks. While this information is helpful in personalizing search results, it also presents data privacy concerns. Clearly communicate what data is collected and let users have a say in its gathering and storage. For instance, Slack customer data remains on the platform and isn’t used to train large language AI models.

• Resource allocation: Semantic search can seem magical, but its power depends on behind-the-scenes tools and resources such as a strong computing infrastructure and its associated costs and power demands, regular updates, and model retraining. These hidden needs can often mean higher upfront costs, which may be a roadblock for smaller organizations.
• Accuracy vs. speed: A deeper search understanding might mean slower search turnaround times. One way to address this is to provide basic results immediately while the system runs deeper analysis in the background.

 

FAQs

What is a semantic search engine?

A semantic search engine understands the context and meaning behind a user’s query. It’s able to provide more relevant and insightful results instead of simply looking for keyword matches.

What are the common use cases for semantic search?

Semantic search is useful for knowledge bases, customer support, AI agents, and other systems in need of more intelligent search results.

How do you cache semantic search?

To cache semantic search, store the processed query embeddings or search results. In the future, when a user enters a similar query, the results can be quickly retrieved without repeating computations.

Does semantic search improve over time?

Yes, semantic search learns and improves when it’s given more data, user feedback, and model updates.

What kind of technology is usually used in semantic search to understand query intent?

To understand query context, semantic search engines typically use machine learning models such as transformers (BERT or GPT).

Is Google a semantic search?

Yes, Google uses semantic search techniques to gain context, improve accuracy, and interpret context and user intent.