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：时间事实，即特定时间段内持续发生的事件，正受到知识图谱（KG）研究社区的越来越多的关注。在质量管理方面，引入时间限制给KG的时间一致性维护和潜在时间冲突检测带来了新的挑战。以往的研究依赖于手动列举时间约束，以检测冲突，这既费时费力，也可能存在粒度问题。我们从时态事实和约束的共同模式出发，提出了一种基于模式的时态约束挖掘方法，PaTeCon。PaTeCon根据候选约束在给定KG上的自动确定的图形模式及其相关的统计信息，而非人类专家来生成时间约束。具体而言，PaTeCon 根据约束的评分信息为候选约束动态追加类别约束。我们在两个基于Wikidata和Freebase的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方法能够生成有价值的时态约束。
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