2012 AutomaticTaxonomyConstructionfr

(Liu et al., 2012) ⇒ Xueqing Liu, Yangqiu Song, Shixia Liu, and Haixun Wang. (2012). “Automatic Taxonomy Construction from Keywords.” In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012). ISBN:978-1-4503-1462-6 doi:10.1145/2339530.2339754

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Author Keywords

Bayesian rose tree; data mining; hierarchical clustering; knowledgebase; learning; nonparametric statistics; query understanding; taxonomy building

Abstract

Taxonomies, especially the ones in specific domains, are becoming indispensable to a growing number of applications. State-of-the-art approaches assume there exists a text corpus to accurately characterize the domain of interest, and that a taxonomy can be derived from the text corpus using information extraction techniques. In reality, neither assumption 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 contexts 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), so that we can derive a domain specific taxonomy from one million keyword phrases inan 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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 AutomaticTaxonomyConstructionfr	Haixun Wang Shixia Liu Yangqiu Song Xueqing Liu			Automatic Taxonomy Construction from Keywords				10.1145/2339530.2339754		2012