Temporal facts, the facts for characterizing events that hold in specific time periods, are attracting rising attention in the knowledge graph (KG) research communities. In terms of quality management, the introduction of time restrictions brings new challenges to maintaining the temporal consistency of KGs and detecting potential temporal conflicts. Previous studies rely on manually enumerated temporal constraints to detect conflicts, which are labor-intensive and may have granularity issues. We start from the common pattern of temporal facts and constraints and propose a pattern-based temporal constraint mining method, PaTeCon. PaTeCon uses automatically determined graph patterns and their relevant statistical information over the given KG instead of human experts to generate time constraints. Specifically, PaTeCon dynamically attaches class restriction to candidate constraints according to their measuring scores.We evaluate PaTeCon on two large-scale datasets based on Wikidata and Freebase respectively. The experimental results show that pattern-based automatic constraint mining is powerful in generating valuable temporal constraints.

翻译：时序事实——用于刻画特定时间段内发生事件的事实——正日益受到知识图谱研究社区的关注。在质量管理方面，时间限制的引入为维护知识图谱的时序一致性及检测潜在时序冲突带来了新挑战。现有研究依赖人工枚举的时序约束进行冲突检测，这种方式不仅劳动密集，且可能存在粒度问题。我们从时序事实与约束的通用模式出发，提出一种基于模式的时序约束挖掘方法PaTeCon。PaTeCon利用自动确定的图模式及其在给定知识图谱上的相关统计信息（而非人类专家）来生成时间约束。具体而言，PaTeCon根据候选约束的度量得分动态附加类别限制。我们在基于Wikidata和Freebase的两个大规模数据集上对PaTeCon进行了评估。实验结果表明，基于模式的自动约束挖掘在生成有价值的时序约束方面效果显著。