Social media networks have evolved as a large repository of short documents and gives the greater challenges to effectively retrieve the content out of it. Many factors were involved in this process such as restricted length of a content, informal use of language (., slangs, abbreviations, styles, etc.) and low contextualization of the user generated content. To meet out the above stated problems, latest studies on context-based information searching have been developed and built on adding semantics to the user generated content into the existing knowledge base. And also, earlier, bag-of-concepts has been used to link the potential noun phrases into existing knowledge sources. Thus, in this paper, we have effectively utilized the relationships among the concepts and equivalence prevailing in the related concepts of the selected named entities by deriving the potential meaning of entities and find the semantic similarity between the named entities with three other potential sources of references (DBpedia, Anchor Texts and Twitter Trends). | Semantic based entity retrieval and disambiguation system for Twitter streams
