基于用户自描述标签的层次分类体系构建方法

模式层知识对于语义万维网的发展非常重要,然而当前开放链接数据(LOD)中模式层知识的数量十分有限,为突破这一局限,提出一种基于社交网络中用户自描述标签的层次分类体系构建方法。该方法首先设计基于搜索引擎的标签分块算法,将描述相同话题的标签划分到同一标签块中,然后采用基于半监督学习的标签传播算法挖掘相同标签块中标签间的上下位关系,最后运用基于启发式规则的贪心算法来构建层次分类体系,从而在社交站点中构建出大规模且高质量的层次分类体系。实验结果表明,该构建方法与现有相关工作相比在准确率、召回率以及F值上均有明显提高。

Taxonomy Construction Based on User Self-describing Tags

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.