@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://aclanthology.org/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.",
}
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<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.</abstract>
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%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
Markdown (Informal)
[Exploring the Limits of Few-Shot Link Prediction in Knowledge Graphs](https://aclanthology.org/2021.eacl-main.245) (Jambor et al., EACL 2021)
ACL