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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. 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.[1] Major web search engines like Google and Bing incorporate some elements of semantic search. In vertical search, LinkedIn publishes their semantic search approach to job search by recognizing and standardizing entities in both queries and documents, e.g., companies, titles and skills, then constructing various entity-awared features based on the entities.