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.[1] 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. Content that ranks well in semantic search is well-written in a natural voice, focuses on the user's intent, and considers related topics that the user may look for in the future.[2]

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.[3] 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.[4]

See also


  1. ^ Bast, Hannah; Buchhold, Björn; Haussmann, Elmar (2016). "Semantic search on text and knowledge bases". Foundations and Trends in Information Retrieval. 10 (2–3): 119–271. doi:10.1561/1500000032. Retrieved 1 December 2018.
  2. ^ Mattar, Nick (January 12, 2020). "Semantic SEO: How and Why". Digital Detroit LLC. Retrieved August 4, 2020.
  3. ^ Dong, Hai (2008). A survey in semantic search technologies. IEEE. pp. 403–408. Retrieved 1 May 2009.
  4. ^ Ruotsalo, T. (May 2012). "Domain Specific Data Retrieval on the Semantic Web". Eswc2012. Lecture Notes in Computer Science. 7295: 422–436. doi:10.1007/978-3-642-30284-8_35. ISBN 978-3-642-30283-1.