Semantic search seeks to improve search accuracy by understanding searcher 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. Author Seth Grimes lists “11 approaches that join semantics to search”, and Hildebrand et al. provide an overview that lists semantic search systems and identifies other uses of semantics in the search process. Semantic search systems consider various points including context of search, location, intent, variation of words, synonyms, generalized and specialized queries, concept matching and natural language queries to provide relevant search results. Major web search engines like Google and Bing incorporate some elements of semantic search. Guha et al. distinguish two major forms of search: navigational and research. In navigational search, the user is using the search engine as a navigation tool to navigate to a particular intended document. Semantic search is not applicable to navigational searches. In research search, the user provides the search engine with a phrase which is intended to denote an object about which the user is trying to gather/research information. There is no particular document which the user knows about and is trying to get to. Rather, the user is trying to locate a number of documents which together will provide the desired information. Semantic search lends itself well with this approach that is closely related with exploratory search. Rather than using ranking algorithms such as Google’s PageRank to predict relevancy, semantic search uses semantics, or the science of meaning in language, to produce highly relevant search results. In most cases, the goal is to deliver the information queried by a user rather than have a user sort through a list of loosely related keyword results. However, Google itself has subsequently also announced its own Semantic Search project. Other authors primarily regard semantic search as a set of techniques for retrieving knowledge from richly structured data sources like ontologies 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 his intent in more detail at query time.
Joost Nusselder is The Content Decoder, a content marketer, dad and loves trying out new tools en tactics. He's been working on a portfolio of niche sites since 2010. Now since 2016 he creates in-depth blog articles together with his team to help loyal readers earn from their own succesful sites.