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.

通过基于模式的时间约束挖掘方法，本研究在知识图谱研究领域开发了一种自动生成有关时间的约束规则的方法，用于解决时间约束对知识图谱一致性的影响，并通过构建两个新的冲突检测基准数据集，证明了该方法在生成有用的时间约束方面的高效性。

时间知识图谱的冲突检测：一种快速约束挖掘算法和新的基准测试

Conflict Detection for Temporal Knowledge Graphs:A Fast Constraint  Mining Algorithm and New Benchmarks

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.

该研究提出了一种基于模式的时间约束挖掘方法（PaTeCon），通过使用图模式和相关的统计信息来生成自动约束，从而解决知识图谱中保持时间一致性和检测潜在时间冲突的挑战。实验结果表明，基于模式的自动约束挖掘方法非常有效。