  Conventional embedding-based models approach event time prediction in
temporal knowledge graphs (TKGs) as a ranking problem. However, they often fall
short in capturing essential temporal relationships such as order and distance.
In this paper, we propose TEILP, a logical reasoning framework that naturally
integrates such temporal elements into knowledge graph predictions. We first
convert TKGs into a temporal event knowledge graph (TEKG) which has a more
explicit representation of time in term of nodes of the graph. The TEKG equips
us to develop a differentiable random walk approach to time prediction.
Finally, we introduce conditional probability density functions, associated
with the logical rules involving the query interval, using which we arrive at
the time prediction. We compare TEILP with state-of-the-art methods on five
benchmark datasets. We show that our model achieves a significant improvement
over baselines while providing interpretable explanations. In particular, we
consider several scenarios where training samples are limited, event types are
imbalanced, and forecasting the time of future events based on only past events
is desired. In all these cases, TEILP outperforms state-of-the-art methods in
terms of robustness.
