Topical key concept extraction from folksonomy through graph-based ranking

Existing studies for concept extraction mainly focus on text corpora and indiscriminately mix numerous topics, which may lead to a knowledge acquisition bottleneck and misconception. We thus propose a novel method for extracting topical key concepts from folksonomy. This method can overcome the aforementioned problems through rich user-generated content and topic-sensitive concept extraction. We first identify topics from folksonomy by using topic models. Tags are then ranked according to importance relative to a certain topic through graph-based ranking. The top-ranking tags are extracted as topical key concepts. The combination of a novel edge weight and preference is proposed in tag importance propagation. The proposed method is applied to different datasets and is found to outperform the state-of-the-art baselines significantly. From the perspectives of parameter influence and case study, the proposed method is feasible and effective.