Evolutionary taxonomy construction from dynamic tag space

Collaborative tagging allows users to tag online resources. We refer to the large database of tags and their relationships as a tag space. In a tag space, the popularity and correlation amongst tags capture the current social interests, and taxonomy is a useful way to organize these tags. As tags change over time, it is imperative to incorporate the temporal tag evolution into the taxonomies. In this paper, we formalize the problem of evolutionary taxonomy generation over a large database of tags. The proposed evolutionary taxonomy framework consists of two key features. Firstly, we develop a novel context-aware edge selection algorithm for taxonomy extraction. Secondly, we propose several algorithms for evolutionary taxonomy fusion. We conduct an extensive performance study using a very large real-life dataset (i.e., Del.ici.ous). The empirical results clearly show that our approach is effective and efficient.