@inproceedings{he-etal-2020-bert,
title = "{BERT}-{MK}: Integrating Graph Contextualized Knowledge into Pre-trained Language Models",
author = "He, Bin and
Zhou, Di and
Xiao, Jinghui and
Jiang, Xin and
Liu, Qun and
Yuan, Nicholas Jing and
Xu, Tong",
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.207",
doi = "10.18653/v1/2020.findings-emnlp.207",
pages = "2281--2290",
abstract = "Complex node interactions are common in knowledge graphs (KGs), and these interactions can be considered as contextualized knowledge exists in the topological structure of KGs. Traditional knowledge representation learning (KRL) methods usually treat a single triple as a training unit, neglecting the usage of graph contextualized knowledge. To utilize these unexploited graph-level knowledge, we propose an approach to model subgraphs in a medical KG. Then, the learned knowledge is integrated with a pre-trained language model to do the knowledge generalization. Experimental results demonstrate that our model achieves the state-of-the-art performance on several medical NLP tasks, and the improvement above MedERNIE indicates that graph contextualized knowledge is beneficial.",
}
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<abstract>Complex node interactions are common in knowledge graphs (KGs), and these interactions can be considered as contextualized knowledge exists in the topological structure of KGs. Traditional knowledge representation learning (KRL) methods usually treat a single triple as a training unit, neglecting the usage of graph contextualized knowledge. To utilize these unexploited graph-level knowledge, we propose an approach to model subgraphs in a medical KG. Then, the learned knowledge is integrated with a pre-trained language model to do the knowledge generalization. Experimental results demonstrate that our model achieves the state-of-the-art performance on several medical NLP tasks, and the improvement above MedERNIE indicates that graph contextualized knowledge is beneficial.</abstract>
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%0 Conference Proceedings
%T BERT-MK: Integrating Graph Contextualized Knowledge into Pre-trained Language Models
%A He, Bin
%A Zhou, Di
%A Xiao, Jinghui
%A Jiang, Xin
%A Liu, Qun
%A Yuan, Nicholas Jing
%A Xu, Tong
%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 he-etal-2020-bert
%X Complex node interactions are common in knowledge graphs (KGs), and these interactions can be considered as contextualized knowledge exists in the topological structure of KGs. Traditional knowledge representation learning (KRL) methods usually treat a single triple as a training unit, neglecting the usage of graph contextualized knowledge. To utilize these unexploited graph-level knowledge, we propose an approach to model subgraphs in a medical KG. Then, the learned knowledge is integrated with a pre-trained language model to do the knowledge generalization. Experimental results demonstrate that our model achieves the state-of-the-art performance on several medical NLP tasks, and the improvement above MedERNIE indicates that graph contextualized knowledge is beneficial.
%R 10.18653/v1/2020.findings-emnlp.207
%U https://aclanthology.org/2020.findings-emnlp.207
%U https://doi.org/10.18653/v1/2020.findings-emnlp.207
%P 2281-2290
Markdown (Informal)
[BERT-MK: Integrating Graph Contextualized Knowledge into Pre-trained Language Models](https://aclanthology.org/2020.findings-emnlp.207) (He et al., Findings 2020)
ACL