Exploring the Limits of Few-Shot Link Prediction in Knowledge Graphs

• URL: http://arxiv.org/abs/2102.03419v1
• Date: Fri, 5 Feb 2021 21:04:31 GMT
• Title: Exploring the Limits of Few-Shot Link Prediction in Knowledge Graphs
• Authors: Dora Jambor, Komal Teru, Joelle Pineau, William L. Hamilton
• Abstract summary: We study a spectrum of models derived by generalizing the current state of the art for few-shot link prediction. We find that a simple zero-shot baseline - which ignores any relation-specific information - achieves surprisingly strong performance. 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.
• Score: 49.6661602019124
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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