  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.
