Semantic search represents a sophisticated approach to information retrieval that seeks to understand the intent and contextual meaning behind a search query, rather than just matching keywords. It aims to improve search accuracy by considering the searcher's intent and the contextual significance of terms as they appear within the searchable dataspace. This method leverages technologies like machine learning and artificial intelligence to interpret natural language more accurately and deliver results that are conceptually relevant to the query. By doing so, semantic search can provide a more intuitive and effective search experience, returning content that aligns with the intended meaning rather than just the literal words used in the search. This technology is particularly useful in fields where precision and context are crucial, such as academic research or industry-specific databases.
In semantic search, the system evaluates the context of a query and uses data from various sources to provide more relevant results. This process involves:
The goal is to return results that are not only relevant to the query but also align with the user's needs and the broader context in which the search occurs. Semantic search is commonly used in modern search engines, enabling them to deliver more precise and contextually aware answers.
Semantic search denotes search with meaning, as distinguished from lexical search where the search engine 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 searcher's 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.
Some authors regard semantic search as a set of techniques for retrieving knowledge from richly structured data sources like ontologies and XML as found on the Semantic Web. Such technologies enable the formal articulation of domain knowledge at a high level of expressiveness and could enable the user to specify their intent in more detail at query time.The articulation enhances content relevance and depth by including specific places, people, or concepts relevant to the query.