Semantic search is a search technique that uses the intent and contextual meaning behind a search query to deliver more relevant results. It is different from lexical search, which looks for literal matches of the query words or variants of them, without understanding the overall meaning of the query. Semantic search seeks to improve search accuracy by understanding the searchers intent and the contextual meaning of terms as they appear in the searchable dataspace, whether on the Web or within a closed system, to generate more relevant results.

Semantic search is made possible through a combination of natural language processing (NLP) and machine learning (ML). These types of artificial intelligence make search results more comprehensive, seamless, and meaningful by understanding exactly what the user is trying to ask and what the user intent is behind the arrangement of keywords, as opposed to simply matching keywords to web pages.

Key features of semantic search include:

• Intent and Context: Semantic search applies user intent, context, and conceptual meanings to match a user query to the corresponding content.

• Vector Search: Semantic search is powered by vector search, which enables it to deliver and rank content based on context relevance and intent relevance. Vector search encodes details of searchable information into fields of related terms or items, or vectors, and then compares vectors to determine which are most similar.

Semantic search is intended to improve the quality of search results by interpreting natural language more accurately and in context. It achieves this by matching search intent to semantic meaning with the help of technologies such as machine learning and artificial intelligence. The role of semantic search is to improve the user’s experience by interpreting the user’s intent to understand their needs and rank results in order of relevance.