  Temporal Knowledge Graphs store events in the form of subjects, relations,
objects, and timestamps which are often represented by dynamic heterogeneous
graphs. Event forecasting is a critical and challenging task in Temporal
Knowledge Graph reasoning that predicts the subject or object of an event in
the future. To obtain temporal embeddings multi-step away in the future,
existing methods learn generative models that capture the joint distribution of
the observed events. To reduce the high computation costs, these methods rely
on unrealistic assumptions of independence and approximations in training and
inference. In this work, we propose SeDyT, a discriminative framework that
performs sequence modeling on the dynamic entity embeddings to solve the
multi-step event forecasting problem. SeDyT consists of two components: a
Temporal Graph Neural Network that generates dynamic entity embeddings in the
past and a sequence model that predicts the entity embeddings in the future.
Compared with the generative models, SeDyT does not rely on any heuristic-based
probability model and has low computation complexity in both training and
inference. SeDyT is compatible with most Temporal Graph Neural Networks and
sequence models. We also design an efficient training method that trains the
two components in one gradient descent propagation. We evaluate the performance
of SeDyT on five popular datasets. By combining temporal Graph Neural Network
models and sequence models, SeDyT achieves an average of 2.4% MRR improvement
when not using the validation set and more than 10% MRR improvement when using
the validation set.
