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：时态事实，即用于描述在特定时间段内发生的事件的事实，正日益受到知识图谱研究领域的关注。在质量管理方面，时间限制的引入为维护知识图谱的时态一致性及检测潜在时态冲突带来了新的挑战。以往的研究依赖人工枚举的时态约束来检测冲突，这种方法劳动密集且可能存在粒度问题。我们从时态事实和约束的常见模式出发，提出了一种基于模式的时态约束挖掘方法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根据候选约束的衡量分数动态附加类别限制。我们在基于Wikidata和Freebase的两个大规模数据集上评估了PaTeCon。实验结果表明，基于模式的自动约束挖掘在生成有价值时态约束方面具有强大的能力。