  Temporal knowledge graph, serving as an effective way to store and model
dynamic relations, shows promising prospects in event forecasting. However,
most temporal knowledge graph reasoning methods are highly dependent on the
recurrence or periodicity of events, which brings challenges to inferring
future events related to entities that lack historical interaction. In fact,
the current moment is often the combined effect of a small part of historical
information and those unobserved underlying factors. To this end, we propose a
new event forecasting model called Contrastive Event Network (CENET), based on
a novel training framework of historical contrastive learning. CENET learns
both the historical and non-historical dependency to distinguish the most
potential entities that can best match the given query. Simultaneously, it
trains representations of queries to investigate whether the current moment
depends more on historical or non-historical events by launching contrastive
learning. The representations further help train a binary classifier whose
output is a boolean mask to indicate related entities in the search space.
During the inference process, CENET employs a mask-based strategy to generate
the final results. We evaluate our proposed model on five benchmark graphs. The
results demonstrate that CENET significantly outperforms all existing methods
in most metrics, achieving at least $8.3\%$ relative improvement of Hits@1 over
previous state-of-the-art baselines on event-based datasets.
