时态知识图谱补全的方法及其进展

Temporal knowledge graph completion:methods and progress

时态知识图谱是将时间信息添加到传统的知识图谱而得到的。近年来,时态知识图谱补全受到了学术界的高度关注,并成为研究热点之一。总结了目前时态知识图谱补全的两大类方法,即基于符号逻辑的方法和基于知识表示学习的方法,比较分析了两类方法的优缺点,展望了未来时态补全方法的发展方向,还总结了7个用于时态知识图谱补全的基准数据集和若干代表性模型在基准数据集上的评测结果。

Temporal knowledge graph(TKG)are obtained by adding the time information of real-world knowledge to classical knowledge graphs.Recently,TKG completion has drawn much attention and become a hot topic in research.Two main methodologies for TKG completion were summarized,one based on symbolic logic whereas and the other based on knowledge representation learning.The pros and cons of these two different methodologies were discussed,highlighting some directions for enhancing TKG completion in future research.Also,seven benchmark datasets for TKG completion and evaluation results of several typical models on the benchmark datasets were introduced.