Definition and Purpose of Semantic Search
– Semantic search focuses on understanding the meaning of search queries.
– It aims to improve search accuracy by considering the searchers’ intent and contextual meaning of terms.
– Semantic search generates more relevant results by analyzing the searchable dataspace.
– Well-written content in a natural voice performs well in semantic search.
– It takes into account related topics that users may search for in the future.
Techniques for Semantic Search
– Semantic search utilizes techniques for retrieving knowledge from structured data sources like ontologies and XML.
– These technologies allow the formal articulation of domain knowledge.
– They enable users to specify their intent in more detail during query time.
– Semantic search techniques can be applied to both text and knowledge bases.
– It involves understanding the semantic relationships between entities.
Related Concepts and Technologies
– Semantic web is closely related to semantic search.
– Semantic unification is another concept associated with semantic search.
– Resource Description Framework (RDF) is commonly used in semantic search.
– Natural language search engines leverage semantic search techniques.
– Semantic queries allow users to express their intent in a more detailed manner.
References
– 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.
– Mattar, Nick (January 12, 2020). ‘Semantic SEO: How and Why’. Digital Detroit LLC.
– Dong, Hai (2008). ‘A survey in semantic search technologies’. IEEE. pp.403–408.
– Ruotsalo, T. (May 2012). ‘Domain Specific Data Retrieval on the Semantic Web’. Eswc2012. Lecture Notes in Computer Science. 7295: 422–436.
– Various workshops and external links related to semantic search.
Additional Concepts and Resources
– Library 2.0 and digital library concepts are relevant to semantic search.
– Digital humanities can benefit from semantic search techniques.
– Metadata plays a crucial role in semantic search.
– Topic map and Web 2.0 are related to semantic search.
– Schema.org provides a common vocabulary for semantic search.
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. 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.
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.
