1. A method comprising: identifying search session logs of a user; segmenting the search session logs into search sessions based at least on a predetermined session time interval; analyzing each of the search sessions to determine a first keyword set and a second keyword set; combining the first keyword set and the second keyword set to form a pair of keyword sets, wherein the pair is represented as at least one of any combination of the first keyword set and the second keyword set; determining a minimum frequency as a smaller of a frequency at which the first keyword set occurs and a frequency at which the second keyword set occurs; comparing a value proportional to the minimum frequency with a frequency at which the pair of keyword sets occur; calculating semantic relevance between the first and the second keyword sets based at least in part on the comparing, wherein the calculating comprises: computing a similarity value between the first and second keyword sets based in part on a mutual information analysis or a cosine similarity analysis; and comparing the similarity value with a similarity threshold, wherein the similarity threshold is determined by: generating a training set which includes a predetermined number of queries obtained from one or more training search sessions; identifying a predetermined number of suggested queries based on an associated relevance of the suggested queries to each query in the training set; categorizing the suggested queries into one or more groups based on the associated relevance; and ascertaining the similarity threshold based on the associated relevance corresponding to at least one of the one or more groups; and displaying one or more semantically relevant keyword sets based on the calculation.
1. A method comprising: identifying search session logs of a user; segmenting the search session logs into search sessions based at least on a predetermined session time interval; analyzing each of the search sessions to determine a first keyword set and a second keyword set; combining the first keyword set and the second keyword set to form a pair of keyword sets, wherein the pair is represented as at least one of any combination of the first keyword set and the second keyword set; determining a minimum frequency as a smaller of a frequency at which the first keyword set occurs and a frequency at which the second keyword set occurs; comparing a value proportional to the minimum frequency with a frequency at which the pair of keyword sets occur; calculating semantic relevance between the first and the second keyword sets based at least in part on the comparing, wherein the calculating comprises: computing a similarity value between the first and second keyword sets based in part on a mutual information analysis or a cosine similarity analysis; and comparing the similarity value with a similarity threshold, wherein the similarity threshold is determined by: generating a training set which includes a predetermined number of queries obtained from one or more training search sessions; identifying a predetermined number of suggested queries based on an associated relevance of the suggested queries to each query in the training set; categorizing the suggested queries into one or more groups based on the associated relevance; and ascertaining the similarity threshold based on the associated relevance corresponding to at least one of the one or more groups; and displaying one or more semantically relevant keyword sets based on the calculation. 6. The method of claim 1 , wherein the one or more semantically relevant keyword sets are determined by the cosine similarity analysis based on a relationship: 
 f uv =< 4 √f m , where f uv is the frequency of the pair, and f m is the minimum frequency, wherein the cosine similarity analysis is conducted based on equation, Cos ⁡ ( q u , q v ) = ∑ j = 1 n ⁢ C n , j * C v , j ∑ j = 1 n ⁢ C n , j 2 * ∑ j = 1 n ⁢ C v , j 2 C u is the number of queries for keyword set u occurring in all sessions, and C v is the number of queries for keyword set v occurring in all sessions.