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- Title
Topical key concept extraction from folksonomy through graph-based ranking.
- Authors
Xue, Han; Qin, Bing; Liu, Ting
- Abstract
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.
- Subjects
DATA extraction; FOLKSONOMIES; RANKING (Statistics); TAGS (Metadata); RANDOM walks
- Publication
Multimedia Tools & Applications, 2016, Vol 75, Issue 15, p8875
- ISSN
1380-7501
- Publication type
Article
- DOI
10.1007/s11042-014-2303-9