  Temporal knowledge graphs, representing the dynamic relationships and
interactions between entities over time, have been identified as a promising
approach for event forecasting. However, a limitation of most temporal
knowledge graph reasoning methods is their heavy reliance 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
state of affairs is often the result of a combination of historical information
and underlying factors that are not directly observable. To this end, we
investigate the limits of historical information for temporal knowledge graph
extrapolation and 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 best match the given
query. Simultaneously, by launching contrastive learning, it trains
representations of queries to probe whether the current moment is more
dependent on historical or non-historical events. These representations further
help train a binary classifier, whose output is a boolean mask, indicating the
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.
