  How can we perform knowledge reasoning over temporal knowledge graphs (TKGs)?
TKGs represent facts about entities and their relations, where each fact is
associated with a timestamp. Reasoning over TKGs, i.e., inferring new facts
from time-evolving KGs, is crucial for many applications to provide intelligent
services. However, despite the prevalence of real-world data that can be
represented as TKGs, most methods focus on reasoning over static knowledge
graphs, or cannot predict future events. In this paper, we present a problem
formulation that unifies the two major problems that need to be addressed for
an effective reasoning over TKGs, namely, modeling the event time and the
evolving network structure. Our proposed method EvoKG jointly models both tasks
in an effective framework, which captures the ever-changing structural and
temporal dynamics in TKGs via recurrent event modeling, and models the
interactions between entities based on the temporal neighborhood aggregation
framework. Further, EvoKG achieves an accurate modeling of event time, using
flexible and efficient mechanisms based on neural density estimation.
Experiments show that EvoKG outperforms existing methods in terms of
effectiveness (up to 77% and 116% more accurate time and link prediction) and
efficiency.
