  Real-world knowledge graphs are often characterized by low-frequency
relations - a challenge that has prompted an increasing interest in few-shot
link prediction methods. These methods perform link prediction for a set of new
relations, unseen during training, given only a few example facts of each
relation at test time. In this work, we perform a systematic study on a
spectrum of models derived by generalizing the current state of the art for
few-shot link prediction, with the goal of probing the limits of learning in
this few-shot setting. We find that a simple zero-shot baseline - which ignores
any relation-specific information - achieves surprisingly strong performance.
Moreover, experiments on carefully crafted synthetic datasets show that having
only a few examples of a relation fundamentally limits models from using
fine-grained structural information and only allows for exploiting the
coarse-grained positional information of entities. Together, our findings
challenge the implicit assumptions and inductive biases of prior work and
highlight new directions for research in this area.
