Semantic search is a search technique that aims to improve search accuracy by understanding the meaning and context behind a user's query, rather than just matching keywords. It uses natural language processing (NLP) and machine learning models to interpret the intent behind a search query and return results that are contextually relevant.
Semantic search systems take into account things like synonyms, word sense disambiguation, and the relationships between words to better understand the user's true intent. This allows the search engine to return more relevant results, even if the query doesn't contain the exact words used in the content.
For example, a semantic search engine can understand that the query "How to fix a broken screen" and "How to repair a shattered display" have the same intent, even though they use different terms. This approach is widely used in modern search engines to improve user experience and the relevance of results.