PaTeCon：一种基于模式的知识图谱时间约束挖掘方法用于冲突检测
PaTeCon: A Pattern-Based Temporal Constraint Mining Method for Conflict Detection on Knowledge Graphs
AI总结：
针对知识图谱时间冲突检测依赖人工枚举约束的问题，提出基于模式的自动挖掘方法PaTeCon，利用图模式与统计信息生成时间约束，在Wikidata和Freebase数据集上验证了其有效性。
摘要：
时间事实，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（KG）研究界的关注。在质量管理方面，时间限制的引入为维护知识图谱的时间一致性以及检测潜在的时间冲突带来了新的挑战。以往的研究依赖人工枚举的时间约束来检测冲突，这既耗费人力又可能存在粒度问题。我们从时间事实和约束的常见模式出发，提出了一种基于模式的时间约束挖掘方法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地为其附加类别限制。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的时间约束方面是有效的。
英文摘要：
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.
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