Knowledge Graph Rule Mining via Transfer Learning

摘要：

　　Mining logical rules from knowledge graphs(KGs)is an important yet challenging task,especially when the relevant data is sparse.Transfer learning is an actively researched area to address the data sparsity issue,where a predictive model is learned for the target domain from that of a similar source domain.In this paper,we propose a novel method for rule learning by employing transfer learning to address the data sparsity issue,in which most relevant source KGs and candidate rules can be automatically selected for transfer.This is achieved by introducing a similarity in terms of embedding representations of entities,relations and rules.Experiments are conducted on some standard KGs.The results show that proposed method is able to learn quality rules even with extremely sparse data and its predictive accuracy outperformed state-of-the-art rule learners(AMIE+and RLvLR),and link prediction systems(TransE and HOLE).

关键词： Knowledge graph Transfer learning Representation learning

作者: Pouya Ghiasnezhad Omran Zhe Wang Kewen Wang

作者单位: Griffith University,Brisbane,QLD,Australia Griffith University,Brisbane,QLD,Australia;State Key Laboratory of Computer Science,Institute of Sof

会议类型: 国际会议

会议名称: The 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (第23届亚太知识发现和数据挖掘国际会议(PAKDD2019)

会议地点: 澳门

会议语种:英文

页码: 489-500

在线出版日期: 2019-04-14（万方平台首次上网日期，不代表论文的发表时间）