document: Semantic similarity between search engine queries using temporal correlation We investigate the idea of finding semantically related search engine queries based on their temporal correlation ; in other words , we infer that two queries are related if their popularities behave similarly over time . To this end , we first define a new measure of the temporal correlation of two queries based on the correlation coefficient of their frequency functions . We then conduct extensive experiments using our measure on two massive query streams from the MSN search engine , revealing that this technique can discover a wide range of semantically similar queries . Finally , we develop a method of efficiently finding the highest correlated queries for a given input query using far less space and time than the naive approach , making real-time implementation possible .
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