Temporal facts, which are used to describe events that occur during specific time periods, have become a topic of increased interest in the field of knowledge graph (KG) research. In terms of quality management, the introduction of time restrictions brings new challenges to maintaining the temporal consistency of KGs. Previous studies rely on manually enumerated temporal constraints to detect conflicts, which are labor-intensive and may have granularity issues. To address this problem, we start from the common pattern of temporal facts and propose a pattern-based temporal constraint mining method, PaTeCon. Unlike previous studies, PaTeCon uses graph patterns and statistical information relevant to the given KG to automatically generate temporal constraints, without the need for human experts. In this paper, we illustrate how this method can be optimized to achieve significant speed improvement. We also annotate Wikidata and Freebase to build two new benchmarks for conflict detection. Extensive experiments demonstrate that our pattern-based automatic constraint mining approach is highly effective in generating valuable temporal constraints.

翻译：时序事实用于描述特定时间段内发生的事件，已成为知识图谱研究领域日益受关注的课题。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带来了新挑战。以往研究依赖人工枚举的时序约束进行冲突检测，这种方法不仅劳动密集，且可能存在粒度问题。为解决此问题，我们从时序事实的常见模式出发，提出了一种基于模式的时序约束挖掘方法PaTeCon。与以往研究不同，PaTeCon利用图模式及与给定知识图谱相关的统计信息自动生成时序约束，无需人类专家干预。本文阐述了如何优化该方法以实现显著的性能提升。我们还为维基数据和Freebase标注数据，构建了两个冲突检测的新基准。大量实验证明，我们基于模式的自动约束挖掘方法在生成有价值的时序约束方面非常高效。