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In this paper, we describe our ensemble-search based approach, Q²C@UST (http://webprojectl.cs.ust.hk/q2c/), for the query classification task for the KDDCUP 2005. There are two aspects to the key difficulties of this problem: one is that the meaning of the queries and the semantics of the predefined categories are hard to determine. The other is that there are no training data for this classification problem. We apply a two-phase framework to tackle the above difficulties. Phase I corresponds to the training phase of machine learning research and phase II corresponds to testing phase. In phase I, two kinds of classifiers are developed as the base classifiers. One is synonym-based and the other is statistics based. Phase II consists of two stages. In the first stage, the queries are enriched such that for each query, its related Web pages together with their category information are collected through the use of search …