Topical Key Concept Extraction from Folksonomy
Xue Han, Bing Qin, Ting Liu, Chao Jian Xiang · International Joint Conference on Natural Language Processing · 2013
Concept extraction is a primary subtask of ontology construction. It is difficult to extract new concepts from traditional text corpus. Moreover, building a single ontology for multiple-topic corpus may lead to misconception. To deal with these problems, this paper proposes a novel framework to extract topical key concepts from folksonomy. Folksonomy is a valuable data source due to real-time update and rich user-generated contents. We first identify topics from folksonomy using topic models. Next the tags are ranked according to their importance for a certain topic by applying topic-specific random walk methods. The top-ranking tags are extracted as topical key concepts. Especially, a novel link weight function which combines the local structure information and global semantic similarity is proposed in importance score propagation. From the perspectives of qualitative and quantitative investigation, our method is feasible and effective.