Semantic search refers to the use of natural language processing (NLP) and artificial intelligence (AI) to understand the intent behind a user's search query and provide more accurate and relevant search results. Semantic search aims to go beyond keyword matching and take into account the meaning behind the words used in a search query.
The significance of semantic search lies in its ability to improve the accuracy and relevance of search results, making it easier for users to find the information they are looking for. Semantic search also enables search engines to better understand the relationships between different pieces of information, which can lead to more comprehensive and insightful search results.
Some use cases of semantic search include: Personalized Search: Semantic search can be used to personalize search results based on a user's search history and preferences. This can improve the user experience by providing more relevant and useful search results. E-commerce: Semantic search can be used in e-commerce to improve product recommendations and search results, making it easier for customers to find the products they are looking for. Knowledge Graphs: Semantic search can be used to create knowledge graphs, which are visual representations of the relationships between different pieces of information.