Learning Concept Hierarchy from Folksonomy
Users often use tags to annotate and categorize web content. A folksonomy is a system of classification derived from the practice and method of collaboratively creating and managing tags. The most significant feature of a folksonomy is that it directly reflects the vocabulary of users. This feature is very useful in tag-based content searching and user browsing. Based on mutual-overlapping measurement of tag’s instance sets, an ontology learning algorithm to construct concept hierarchy from folksonomy is proposed. A case study of datasets from a famous Chinese e-business website taobao is carried out. The precision, valid, recall and F-measure rates of the constructed concept hierarchy are 54％, 84％, 100％ and 70％ respectively. The experimental results on real world datasets show that the proposed method is feasible.
Folksonomy Ontology Learning Concept Hierarchy
Shubin Cai Heng Sun Sishan Gu Zhong Ming
Software Engineering Department Shenzhen University Shenzhen, 518060, China Computer Science Department Jinan University Guangzhou, 510632, China Computer Science Department Sun Yat-sen University Guangzhou, 510275, China
国际会议
重庆
英文
47-51
2011-10-21（万方平台首次上网日期，不代表论文的发表时间）