  Temporal knowledge graphs (TKGs) can effectively model the ever-evolving
nature of real-world knowledge, and their completeness and enhancement can be
achieved by reasoning new events from existing ones. However, reasoning
accuracy is adversely impacted due to an imbalance between new and recurring
events in the datasets. To achieve more accurate TKG reasoning, we propose an
attention masking-based contrastive event network (AMCEN) with local-global
temporal patterns for the two-stage prediction of future events. In the
network, historical and non-historical attention mask vectors are designed to
control the attention bias towards historical and non-historical entities,
acting as the key to alleviating the imbalance. A local-global message-passing
module is proposed to comprehensively consider and capture multi-hop structural
dependencies and local-global temporal evolution for the in-depth exploration
of latent impact factors of different event types. A contrastive event
classifier is used to classify events more accurately by incorporating
local-global temporal patterns into contrastive learning. Therefore, AMCEN
refines the prediction scope with the results of the contrastive event
classification, followed by utilizing attention masking-based decoders to
finalize the specific outcomes. The results of our experiments on four
benchmark datasets highlight the superiority of AMCEN. Especially, the
considerable improvements in Hits@1 prove that AMCEN can make more precise
predictions about future occurrences.
