9. A method for identifying candidate queries related to a trending topic based on a user query, the method comprising: generating a data structure that includes entries relating trending topics with words and named entities located in content of one or more real-time content sources by receiving documents from the one or more real-time content sources, segmenting the received documents into words and named entities, organizing the received documents into two or more portions of documents each defined by a time interval within a historical time period, determining a volume for each of a predetermined number of topics, and for each of the predetermined number of topics: calculating a mean volume by taking an average of the determined volumes for every portion within the historical time period, calculating a deviation between a determined volume associated with a portion defined by a most recent time interval of the historical time period to the mean volume, and identifying one of the predetermined number of topics as a trending topic when the topic is associated with a determined volume that has a calculated deviation from the mean volume that exceeds a threshold value; storing the generated data structure in a database accessible to a query suggestion module; receiving a user query at the query recommendation module; matching contents of the received user query to one or more of the trending topics stored within the database; retrieving one or more named entities associated with the matched one or more trending topics; and returning the retrieved one or more named entities associated with the matched one or more trending topics.
9. A method for identifying candidate queries related to a trending topic based on a user query, the method comprising: generating a data structure that includes entries relating trending topics with words and named entities located in content of one or more real-time content sources by receiving documents from the one or more real-time content sources, segmenting the received documents into words and named entities, organizing the received documents into two or more portions of documents each defined by a time interval within a historical time period, determining a volume for each of a predetermined number of topics, and for each of the predetermined number of topics: calculating a mean volume by taking an average of the determined volumes for every portion within the historical time period, calculating a deviation between a determined volume associated with a portion defined by a most recent time interval of the historical time period to the mean volume, and identifying one of the predetermined number of topics as a trending topic when the topic is associated with a determined volume that has a calculated deviation from the mean volume that exceeds a threshold value; storing the generated data structure in a database accessible to a query suggestion module; receiving a user query at the query recommendation module; matching contents of the received user query to one or more of the trending topics stored within the database; retrieving one or more named entities associated with the matched one or more trending topics; and returning the retrieved one or more named entities associated with the matched one or more trending topics. 11. The method of claim 9 , wherein storing the generated data structure in a database accessible to the query recommendation module includes: storing a first data structure associated with a first time interval in which the content of the real-time content source(s) is received; and replacing the first data structure with a second data structure associated with a second time interval in which the content of the real-time content source(s) is received.