14. An article of manufacture comprising: at least one computer usable non-transitory device having computer readable program code logic tangibly embodied therein to execute instructions in one or more processing units for merging search results, said computer readable program code logic, when executing, performing the following: identifying a query from a user; splitting the query into sub-queries; for each of the sub-queries, determining a user importance value for said each sub-query to quantify how important said each sub-query is to the user relative to how important the others of the sub-queries are to the user, executing said each sub-query to obtain a search result for said each sub-query, and using the user importance value determined for said each sub-query to assign a user relevance score to the search result obtained for said each sub-query; and combining the search results for the sub-queries based on the user relevance scores assigned to the search results to obtain merged search results; and wherein: the execution of each of the sub-queries includes identifying a multitude of search results for at least one of the sub-queries; the combining includes grouping said multitude of search results into a plurality of clusters, and computing a relevance score for each of said clusters, wherein each cluster represents a high level entity; the determining a user importance value for said each sub-query includes identifying one of the sub-queries as a primary focus, and identifying another of the sub-queries as providing context, computing a probability p(Q) of each of the sub-queries, and computing information content of each of the sub-queries as log p(Q); and the combining the search results further includes, once the information content of the sub-queries are computed, merging the search results.
15. The article of manufacture according to claim 14 , wherein: the execution of each of the sub-queries includes identifying a multitude of entities for the at least one of the sub-queries; and the combining includes grouping said multitude of entities into the plurality of clusters, and merging the clusters based on the relevance scores computed for the clusters.