  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.
