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Defining a measure of similarity between queries is an interesting and difficult problem. A reliable query-similarity measure can be used in a variety of applications such as query recommendation, query expansion, and advertising. In this paper, we exploit the information present in query logs in order to develop a measure of semantic similarity between queries. Our approach relies on the concept of the query-flow graph. The query-flow graph aggregates query reformulations from many users: nodes in the graph represent queries, and two queries are connected if they are likely to appear as part of the same search goal. Our query similarity measure is obtained by projecting the graph (or appropriate subgraphs of it) on a low-dimensional Euclidean space. Our experiments show that the measure we obtain captures a notion of semantic similarity between queries and it is useful for diversifying query recommendations.