@inproceedings{jambor-etal-2021-exploring,
    title = "Exploring the Limits of Few-Shot Link Prediction in Knowledge Graphs",
    author = "Jambor, Dora  and
      Teru, Komal  and
      Pineau, Joelle  and
      Hamilton, William L.",
    editor = "Merlo, Paola  and
      Tiedemann, Jorg  and
      Tsarfaty, Reut",
    booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
    month = apr,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.eacl-main.245/",
    doi = "10.18653/v1/2021.eacl-main.245",
    pages = "2816--2822",
    abstract = "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."
}