Semantic search in information retrieval (IR) aims to improve search accuracy by understanding the meaning or intent behind a query, rather than relying solely on keyword matching. This involves analyzing the context and relationships between terms to deliver more relevant results based on the user’s needs.
For example, a semantic search system might recognize that "heart disease" and "cardiac illness" refer to the same concept, even though they are different terms. By focusing on meaning rather than exact matches, semantic search can return more accurate and contextually appropriate results.
To achieve semantic search, modern IR systems often use techniques like word embeddings, neural networks, and natural language processing (NLP) to analyze and understand the relationships between terms. These systems are more capable of handling ambiguous or complex queries, improving the user experience by providing results that align more closely with the user’s true intent.