What Is Semantic Search: Definition, Examples, And Usefulness

When you type a search query, you expect more than just a list of results that happen to contain the same words—you want answers that actually make sense. Whether you’re looking for recommendations, definitions, or something specific, modern search technology has evolved to recognize connections, context, and deeper meanings behind queries. This shift is changing how we find information, making search engines smarter and more intuitive than ever.

This deeper understanding of language and intent not only improves the quality of results but also transforms how users interact with search. As a result, semantic search offers several practical benefits that enhance both accuracy and usability.

Semantic search benefits

The main benefit is the semantic search ability to find results even if the entered search terms are not precise. Based on context, the search is able to provide relevant results despite the inclusion of vague search terms. Another benefit is that people can use descriptions as search terms in case they can’t figure out the proper naming conventions or simply forget the proper search term. This significantly improves the user experience thanks to the removal of frustration during a search session.

Semantic search example

For a typical keyword search, a customer may write keywords like ‘sweater’ in the search bar to find a sweater. However, the semantic search will better serve queries like ‘warm clothing’ or ‘How can I keep my body warm in the winter?’. It will understand the intent of the input keywords and come up with accurate results for the topic at hand while maintaining high search relevance. The most obvious use case of this is in internet search engines, such as Google semantic search.

Difference between keyword search and semantic search

A keyword search retrieves all the documents from the database that have a specific search term present in the query. Unlike keyword search, the semantic search takes into account the meaning of the words according to their context. Let’s compare the two side by side.

Difference between lexical search and semantic search

Lexical search involves retrieving documents that contain exact matches or variants of the query words without interpreting the overall meaning of the query. In contrast, semantic search processes the query to deliver results that align with the searcher’s intent.

For example, a search for “shoe store near me” in a semantic search engine yields results for the closest shoe stores, while a lexical search might return stores named ‘Near Me’ or located in a place called ‘Near Me’.

The technology behind semantic search

Semantic search is kind of like a super-smart buddy for your search queries. Instead of just looking for exact words you type in, semantic search tries to figure out what you really mean. It’s like having a conversation where the other person gets the gist of what you’re saying, even if you don’t use the perfect words.

So, how does it do this cool stuff? It uses a mix of clever tech like vector search, artificial intelligence (AI), machine learning (ML), and natural language processing (NLP). Think of AI and ML as the brains that learn from mistakes and improve over time at figuring out what you’re looking for. NLP, on the other hand, is all about making your search box smart enough to really ‘get’ what your products are all about.

In simple terms, semantic search is like a step up from the old-school keyword search. It doesn’t just look at the words; it tries to understand the whole idea behind your search. This means you end up with search results that are spot on, because it’s all about catching the intent and context of what you’re asking. It’s a complex dance of understanding language, knowing how users think, and using some pretty advanced algorithms to bring you the best search results possible.

Let’s take an in-depth look at what else semantic search includes to perform its job.

1. Natural language processing (NLP)

• Tokenization and lemmatization: The search query is broken down into individual elements (tokens) and reduced to their base or dictionary form (lemmatization).
• Part-of-speech tagging: Identifies whether a word is a noun, verb, adjective, etc., to understand the role each word plays in the query.
• Dependency parsing: Analyzes the grammatical structure of a sentence, helping to understand how different words in a query relate to each other.

2. Semantic analysis

• Named entity recognition (NER): Identifies and categorizes key information in text into predefined categories like names of people, organizations, locations, etc.
• Sentiment analysis: Determines the sentiment or tone behind a query, which can be particularly important in understanding the user’s intent.
• Word sense disambiguation: Determines the meaning of a word based on its context, crucial for understanding queries with polysemous words (words with multiple meanings).

3. Knowledge graphs and ontologies

• A knowledge graph is a structured representation of facts about entities (like people, places, and things) and their interrelations.
• Ontologies define the types, properties, and interrelationships of the entities in a domain.

These structures enable the system to understand the context and relationships between different concepts.

4. Machine learning and deep learning

• Vector space models: Words and phrases are converted into vectors in a high-dimensional space. Words with similar meanings are closer together in this space.
• Neural networks: Used for understanding complex patterns in data. Transformer-based models like BERT (Bidirectional Encoder Representations from Transformers) are particularly effective in understanding the context of a query.

5. Indexing and retrieval

• Semantic indexing: Unlike traditional indexing which focuses on keywords, semantic indexing involves understanding the context and topics of a document.
• Query expansion: Automatically modifies a query to include synonyms, related terms, or more specific terms based on the semantic understanding of the original query.

6. Ranking algorithms

Uses a combination of semantic relevance and other factors (like page authority, freshness, user engagement) to rank search results.

7. Feedback loops

Continuous learning from user interactions (clicks, time spent on a page, etc.) to refine and improve the accuracy of search results.

Semantic search use cases

Semantic search technology, with its ability to understand the context and meaning behind queries, has numerous applications across different industries. Here are five key use cases:

Conclusion

So, is semantic search the right option for you? It can be a game changer for many e-commerce websites, but also other types of websites if applied correctly, but it might also be considered redundant if deployed in the wrong use case. Consider using it if you think it can bring something of value to your business and improve your website’s user satisfaction. Other businesses might benefit the most from solutions like Luigi’s Box since they provide an affordable solution for e-commerce product search & discovery.

Frequently asked questions