  Temporal knowledge graphs (TKGs) have been identified as a promising approach
to represent the dynamics of facts along the timeline. The extrapolation of TKG
is to predict unknowable facts happening in the future, holding significant
practical value across diverse fields. Most extrapolation studies in TKGs focus
on modeling global historical fact repeating and cyclic patterns, as well as
local historical adjacent fact evolution patterns, showing promising
performance in predicting future unknown facts. Yet, existing methods still
face two major challenges: (1) They usually neglect the importance of
historical information in KG snapshots related to the queries when encoding the
local and global historical information; (2) They exhibit weak anti-noise
capabilities, which hinders their performance when the inputs are contaminated
with noise.To this end, we propose a novel \blue{Lo}cal-\blue{g}lobal
history-aware \blue{C}ontrastive \blue{L}earning model (\blue{LogCL}) for TKG
reasoning, which adopts contrastive learning to better guide the fusion of
local and global historical information and enhance the ability to resist
interference. Specifically, for the first challenge, LogCL proposes an
entity-aware attention mechanism applied to the local and global historical
facts encoder, which captures the key historical information related to
queries. For the latter issue, LogCL designs four historical query contrast
patterns, effectively improving the robustness of the model. The experimental
results on four benchmark datasets demonstrate that LogCL delivers better and
more robust performance than the state-of-the-art baselines.
