Definition
Semantic search is a search engine technology that can comprehend the meaning of words and phrases. A semantic search result delivers content matching the meaning of a query, as against a content that literally matches words in the query. Its search engine has the power of understanding words from the searcher’s intent and their search context. It is designed to enhance the quality of search results by interpreting natural language more accurately and in context. Semantic search delivers results by using machine learning and artificial intelligence to match search intent to semantic meaning [1].
Let’s take a
look at the following example that compares a traditional keyword search result
with a semantic search result;
Customer Query:
"Fine ankara for wedding guest"
Traditional keyword
search results:
Returns only
items with exact words "fine," "ankara,"
"wedding," "guest"
Misses relevant
products with different terminology
Limited to
literal text matching
But with semantic
search understanding and results, the AI understands that:
"Fine"
means high quality, elegant, beautiful
"Ankara”
means African print fabric clothing
"Wedding
guest" means formal occasion requiring appropriate attire avoiding
white/cream colors that has to do with a bride.
Various stages involved in semantic search process [2]
Origin
The origin of semantic search can be traced back to 1999, when Tim Berners-Lee first introduced the idea of the semantic web. Since then, the term “semantic search” has referred to many different technologies used in query processing [3].
Context and
Usage
Semantic search is
used in many applications across various industries such as in e-commerce and
enterprise search.
In e-commerce platforms, semantic search improves product search, enabling more relevant and accurate product discovery. while in Enterprise search, semantic search enhances productivity and decision-making by assisting employees to quickly and efficiently find relevant information within company databases, intranets, and knowledge repositories [4].
Why it Matters
Semantic search
is important as it enables a wider search scope. Its vector search capability
allows it to provide a more intuitive search experience where a query's context
and intent yield results.
Because semantic search algorithms continue "learning" with a variety of key performance indicators (KPIs), such as conversion rates and bounce rates, semantic search helps boost user satisfaction.
In Practice
Explore AI that uses sophisticated algorithms to deliver a semantic search experience is a good example of a real-life case study of semantic search in practice. Explore AI can comprehend the meaning and context behind search queries, unlike traditional search engines that bank on keyword matching. This enables the search engine to deliver more relevant and accurate results, improving user experience significantly [5].
See Also
Related NLP and Text Processing terms:
- Semantic AI: Relating to the meaning and interpretation of words, phrases, or symbols
- Sentiment Analysis: Process of determining emotional tone or opinion expressed in text
- Speech Analytics: Extracting insights and patterns from audio speech data
- Speech Recognition: Converting spoken language into written text
- Syntax Analysis: Understanding sentence structure
References
- Elasticsearch. (n.d). What is semantic search?
- Belagatti, P. (2025). Semantic Search: What Is It + How Does It Work?
- Lemoine, J. (2024). The past, present, and future of semantic search
- Google cloud. (n.d). Semantic Search.
- Deepgram. (2025). Explore AI: Semantic search supercharging video discovery on YouTube channels.