%0 Conference Proceedings %T Exploring the Limits of Few-Shot Link Prediction in Knowledge Graphs %A Jambor, Dora %A Teru, Komal %A Pineau, Joelle %A Hamilton, William L. %Y Merlo, Paola %Y Tiedemann, Jorg %Y Tsarfaty, Reut %S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume %D 2021 %8 April %I Association for Computational Linguistics %C Online %F jambor-etal-2021-exploring %X 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. %R 10.18653/v1/2021.eacl-main.245 %U https://aclanthology.org/2021.eacl-main.245 %U https://doi.org/10.18653/v1/2021.eacl-main.245 %P 2816-2822