  Most real-world knowledge graphs are characterized by a long-tail relation
frequency distribution where a significant fraction of relations occurs only a
handful of times. This observation has given rise to recent interest in
low-shot learning methods that are able to generalize from only a few examples.
The existing approaches, however, are tailored to static knowledge graphs and
not easily generalized to temporal settings, where data scarcity poses even
bigger problems, e.g., due to occurrence of new, previously unseen relations.
We address this shortcoming by proposing a one-shot learning framework for link
prediction in temporal knowledge graphs. Our proposed method employs a
self-attention mechanism to effectively encode temporal interactions between
entities, and a network to compute a similarity score between a given query and
a (one-shot) example. Our experiments show that the proposed algorithm
outperforms the state of the art baselines for two well-studied benchmarks
while achieving significantly better performance for sparse relations.
