Automatic Taxonomy Construction from Keywords via Scalable Bayesian Rose Trees

作者：Song, Yangqiu^*; Liu, Shixia; Liu, Xueqing; Wang, Haixun

来源：IEEE Transactions on Knowledge and Data Engineering, 2015, 27(7): 1861-1874.

DOI：10.1109/TKDE.2015.2397432

• Bayesian rose tree
• hierarchical clustering
• short text conceptualization
• keyword taxonomy building

In this paper, we study a challenging problem of deriving a taxonomy from a set of keyword phrases. A solution can benefit many real-world 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(n(2) log n) to O(n log n) using a nearest-neighbor-based approximation, 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 web search query taxonomy illustrates the usefulness of our approach for specific domains.

• 出版日期2015-7
• 单位清华大学