With the explosive growth of the Internet resources, web search engines have become the most popular web service to help people find relevant information on a given topic on the web. However, many novice users have difficulty in formulating effective queries for their information needs, in the worst case, users are even not sure what exactly their specific information is. Under this condition, query suggestion technology is proposed to recommend a list of related queries for a given initial query submitted to a search engine, which can help users specify their information needs better. In this paper, we present an effective approach for query suggestion. For a given Chinese web query, the approach can not only identify related queries already existed in the log of past submitted queries to search engines but also use synonyms extracted from web-based corpuses to construct new related queries. Our method not only discovers the related queries, but also ranks them according to the degree of their relatedness, effectiveness and freshness. The achieved performance shows that the proposed approach is very effective in recommending related queries for high-frequency queries, moreover, it also performs reasonably well on less frequent queries.