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.- Anthology ID:
- 2021.eacl-main.245
- Volume:
- Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
- Month:
- April
- Year:
- 2021
- Address:
- Online
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2816–2822
- Language:
- URL:
- https://aclanthology.org/2021.eacl-main.245
- DOI:
- 10.18653/v1/2021.eacl-main.245
- Cite (ACL):
- Dora Jambor, Komal Teru, Joelle Pineau, and William L. Hamilton. 2021. Exploring the Limits of Few-Shot Link Prediction in Knowledge Graphs. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 2816–2822, Online. Association for Computational Linguistics.
- Cite (Informal):
- Exploring the Limits of Few-Shot Link Prediction in Knowledge Graphs (Jambor et al., EACL 2021)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2021.eacl-main.245.pdf
- Data
- Wiki-One