Taxonomies, especially taxonomies in speciﬁc domains, are becoming indispensable to a growing number of applications. State-of-the-art approaches assume that there exists a text corpus that accurately characterizes the domain of interest, and that a taxonomy can be derived from the text corpus using information extraction techniques. In reality, neither of the two assumptions is valid, especially for highly focused or fast-changing domains. In this paper, we study a challenging problem: Deriving a taxonomy from a set of keyword phrases. A solution can beneﬁt many real life applications because i) keywords give users the ﬂexibility and ease to characterize a speciﬁc domain; and ii) in many applications, such as online advertisements, the domain of interest is already represented by a set of keywords. However, it is impossible to create a taxonomy out of a keyword set itself. We argue that additional knowledge and context are needed. To this end, we ﬁrst use a general purpose knowledgebase and keyword search to supply the required knowledge and context. Then we develop a Bayesian approach to build a hierarchical taxonomy for a given set of keywords. We reduce the complexity of previous hierarchical clustering approaches from O(n2 log n) to O(n log n), so that we can derive a domain speciﬁc taxonomy from one million keyword phrases in less than an hour. Finally, we conduct comprehensive large scale experiments to show the effectiveness and efficiency of our approach. A real life example of building an insurance-related query taxonomy illustrates the usefulness of our approach for speciﬁc domains.