@inproceedings{zhang-etal-2020-pretrain,
title = "Pretrain-{KGE}: Learning Knowledge Representation from Pretrained Language Models",
author = "Zhang, Zhiyuan and
Liu, Xiaoqian and
Zhang, Yi and
Su, Qi and
Sun, Xu and
He, Bin",
editor = "Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.25",
doi = "10.18653/v1/2020.findings-emnlp.25",
pages = "259--266",
abstract = "Conventional knowledge graph embedding (KGE) often suffers from limited knowledge representation, leading to performance degradation especially on the low-resource problem. To remedy this, we propose to enrich knowledge representation via pretrained language models by leveraging world knowledge from pretrained models. Specifically, we present a universal training framework named \textit{Pretrain-KGE} consisting of three phases: semantic-based fine-tuning phase, knowledge extracting phase and KGE training phase. Extensive experiments show that our proposed Pretrain-KGE can improve results over KGE models, especially on solving the low-resource problem.",
}
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<abstract>Conventional knowledge graph embedding (KGE) often suffers from limited knowledge representation, leading to performance degradation especially on the low-resource problem. To remedy this, we propose to enrich knowledge representation via pretrained language models by leveraging world knowledge from pretrained models. Specifically, we present a universal training framework named Pretrain-KGE consisting of three phases: semantic-based fine-tuning phase, knowledge extracting phase and KGE training phase. Extensive experiments show that our proposed Pretrain-KGE can improve results over KGE models, especially on solving the low-resource problem.</abstract>
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%0 Conference Proceedings
%T Pretrain-KGE: Learning Knowledge Representation from Pretrained Language Models
%A Zhang, Zhiyuan
%A Liu, Xiaoqian
%A Zhang, Yi
%A Su, Qi
%A Sun, Xu
%A He, Bin
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F zhang-etal-2020-pretrain
%X Conventional knowledge graph embedding (KGE) often suffers from limited knowledge representation, leading to performance degradation especially on the low-resource problem. To remedy this, we propose to enrich knowledge representation via pretrained language models by leveraging world knowledge from pretrained models. Specifically, we present a universal training framework named Pretrain-KGE consisting of three phases: semantic-based fine-tuning phase, knowledge extracting phase and KGE training phase. Extensive experiments show that our proposed Pretrain-KGE can improve results over KGE models, especially on solving the low-resource problem.
%R 10.18653/v1/2020.findings-emnlp.25
%U https://aclanthology.org/2020.findings-emnlp.25
%U https://doi.org/10.18653/v1/2020.findings-emnlp.25
%P 259-266
Markdown (Informal)
[Pretrain-KGE: Learning Knowledge Representation from Pretrained Language Models](https://aclanthology.org/2020.findings-emnlp.25) (Zhang et al., Findings 2020)
ACL