@inproceedings{jiang-etal-2023-text,
title = "Text Augmented Open Knowledge Graph Completion via Pre-Trained Language Models",
author = "Jiang, Pengcheng and
Agarwal, Shivam and
Jin, Bowen and
Wang, Xuan and
Sun, Jimeng and
Han, Jiawei",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.709",
doi = "10.18653/v1/2023.findings-acl.709",
pages = "11161--11180",
abstract = "The mission of open knowledge graph (KG) completion is to draw new findings from known facts. Existing works that augment KG completion require either (1) factual triples to enlarge the graph reasoning space or (2) manually designed prompts to extract knowledge from a pre-trained language model (PLM), exhibiting limited performance and requiring expensive efforts from experts. To this end, we propose TagReal that automatically generates quality query prompts and retrieves support information from large text corpora to probe knowledge from PLM for KG completion. The results show that TagReal achieves state-of-the-art performance on two benchmark datasets. We find that TagReal has superb performance even with limited training data, outperforming existing embedding-based, graph-based, and PLM-based methods.",
}
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<abstract>The mission of open knowledge graph (KG) completion is to draw new findings from known facts. Existing works that augment KG completion require either (1) factual triples to enlarge the graph reasoning space or (2) manually designed prompts to extract knowledge from a pre-trained language model (PLM), exhibiting limited performance and requiring expensive efforts from experts. To this end, we propose TagReal that automatically generates quality query prompts and retrieves support information from large text corpora to probe knowledge from PLM for KG completion. The results show that TagReal achieves state-of-the-art performance on two benchmark datasets. We find that TagReal has superb performance even with limited training data, outperforming existing embedding-based, graph-based, and PLM-based methods.</abstract>
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%0 Conference Proceedings
%T Text Augmented Open Knowledge Graph Completion via Pre-Trained Language Models
%A Jiang, Pengcheng
%A Agarwal, Shivam
%A Jin, Bowen
%A Wang, Xuan
%A Sun, Jimeng
%A Han, Jiawei
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F jiang-etal-2023-text
%X The mission of open knowledge graph (KG) completion is to draw new findings from known facts. Existing works that augment KG completion require either (1) factual triples to enlarge the graph reasoning space or (2) manually designed prompts to extract knowledge from a pre-trained language model (PLM), exhibiting limited performance and requiring expensive efforts from experts. To this end, we propose TagReal that automatically generates quality query prompts and retrieves support information from large text corpora to probe knowledge from PLM for KG completion. The results show that TagReal achieves state-of-the-art performance on two benchmark datasets. We find that TagReal has superb performance even with limited training data, outperforming existing embedding-based, graph-based, and PLM-based methods.
%R 10.18653/v1/2023.findings-acl.709
%U https://aclanthology.org/2023.findings-acl.709
%U https://doi.org/10.18653/v1/2023.findings-acl.709
%P 11161-11180
Markdown (Informal)
[Text Augmented Open Knowledge Graph Completion via Pre-Trained Language Models](https://aclanthology.org/2023.findings-acl.709) (Jiang et al., Findings 2023)
ACL