queries are usually short and ambiguous. Subtopic mining plays an important role in understanding user's search intent and has attracted many researchers' attention. In this paper, we describe our approach to identify users' intents from query logs, which is a subtopic mining subtask of the NTCIR-9 Intent task for Chinese. We extract queries that are semantically related to the original query from query log, measure their similarities based on their relationship with urls, and cluster them into groups which represent different subtopics. In the experiment section, we show the results of our method evaluated by the organizers and a case study for one of the NTCIR-9 intent queries. The results shows that most found intents are different. The proposed method is easy to implement in real applications and can be computed quickly.