Abstract: Knowledge on schema level is vital for the development of semantic Web.However,the number of schema knowledge is limited in current linking open data (LOD).To optimize the issue,this paper proposed an approach for constructing a taxonomy using user self-describing tags in social network.This approach first designs a tag blocking algorithm based on search engine to partition tags into the same block,which describes the same topic.Then,it uses a label propagation algorithm based on the semi-supervised learning to detect hypernym relation between tags in the same block.Finally,it applies a greedy algorithm based on heuristic rules to construct a taxonomy.A large scale and high-quality taxonomy can be constructed after applying the proposed approach in social Web sites.The experimental results show that,compared with the existing related work,the proposed approach performs better in terms of precision,recall and F-score.

Key words: Knowledge on schema level,User self-describing tags,Taxonomy,Label propagation