Semantic Search<br />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. 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.<br />
Current Implementations<br /><ul><li>SenseBot (www.sensebot.net) has developed a Firefox plugin that creates a semantic term cloud based on the search results.
Deepdyve (www.deepdyve.com) uses a semantic search engine for academic content (scientific, technical and medical research).
Powerset (www.powerset.com) is a semantic search engine that is specific to Wikipedia content.
Cognition (www.cognition.com) is a semantic map of the English language that can be used to better understand the user-entered terms based on the context. It provided data in 4 areas: Legal, Health, Wiki and the Bible.</li></li></ul><li>In the future…<br /><ul><li>Semantic Search will change the search engines we use today. By understanding a language and the</li></ul> meaning of the words, results will be more specific and accurate. <br />Search engines will no longer provide results based on proximity of words and popularity of the site/item.<br />With the computational knowledge of the language, there will be an increase in the quality of automated translations and speech recognition<br />