Collaborative querying seeks to help users formulate an accurate query to a search engine by sharing expert knowledge or other users' search experiences. One approach to accomplish collaborative querying is to cluster related queries which are stored in query logs and use the related queries as recommendations to users. Here, the kernel step is to identify the similarity between queries. This paper describes a system that supports collaborative querying among its users. The system operates by clustering and recommending related queries to users using a hybrid query similarity identification approach. The system employs a graph approach to visualize the query recommendations.