Semantic search refers to a type of search that focuses on understanding the meaning behind a query and the intent of the user, rather than relying solely on matching keywords or phrases. It involves using contextual understanding and knowledge of language to provide more relevant and meaningful search results.
Traditional search engines primarily rely on keyword-based matching, where the search engine looks for documents or web pages that contain the exact keywords or phrases entered by the user. However, semantic search goes beyond this approach by considering the context, meaning, and relationships between words and phrases in a query to deliver more accurate and comprehensive search results.
Semantic search incorporates various techniques, such as natural language processing (NLP), machine learning, and artificial intelligence (AI), to understand the intent of the user’s query and provide results that are conceptually related, rather than just keyword-matching results. It can take into account synonyms, related concepts, contextual information, and user intent to deliver more relevant and meaningful search results.
Semantic search is commonly used in various applications, including web search engines, digital assistants, chatbots, recommendation systems, and information retrieval systems, to enhance the accuracy and relevance of search results and improve the overall search experience for users.