@inproceedings{wang-etal-2023-noisy,
title = "Noisy Positive-Unlabeled Learning with Self-Training for Speculative Knowledge Graph Reasoning",
author = "Wang, Ruijie and
Li, Baoyu and
Lu, Yichen and
Sun, Dachun and
Li, Jinning and
Yan, Yuchen and
Liu, Shengzhong and
Tong, Hanghang and
Abdelzaher, Tarek",
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.153",
doi = "10.18653/v1/2023.findings-acl.153",
pages = "2440--2457",
abstract = "This paper studies speculative reasoning task on real-world knowledge graphs (KG) that contain both false negative issue (i.e., potential true facts being excluded) and false positive issue (i.e., unreliable or outdated facts being included). State-of-the-art methods fall short in the speculative reasoning ability, as they assume the correctness of a fact is solely determined by its presence in KG, making them vulnerable to false negative/positive issues. The new reasoning task is formulated as a noisy Positive-Unlabeled learning problem. We propose a variational framework, namely nPUGraph, that jointly estimates the correctness of both collected and uncollected facts (which we call label posterior) and updates model parameters during training. The label posterior estimation facilitates speculative reasoning from two perspectives. First, it improves the robustness of a label posterior-aware graph encoder against false positive links. Second, it identifies missing facts to provide high-quality grounds of reasoning. They are unified in a simple yet effective self-training procedure. Empirically, extensive experiments on three benchmark KG and one Twitter dataset with various degrees of false negative/positive cases demonstrate the effectiveness of nPUGraph.",
}
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<abstract>This paper studies speculative reasoning task on real-world knowledge graphs (KG) that contain both false negative issue (i.e., potential true facts being excluded) and false positive issue (i.e., unreliable or outdated facts being included). State-of-the-art methods fall short in the speculative reasoning ability, as they assume the correctness of a fact is solely determined by its presence in KG, making them vulnerable to false negative/positive issues. The new reasoning task is formulated as a noisy Positive-Unlabeled learning problem. We propose a variational framework, namely nPUGraph, that jointly estimates the correctness of both collected and uncollected facts (which we call label posterior) and updates model parameters during training. The label posterior estimation facilitates speculative reasoning from two perspectives. First, it improves the robustness of a label posterior-aware graph encoder against false positive links. Second, it identifies missing facts to provide high-quality grounds of reasoning. They are unified in a simple yet effective self-training procedure. Empirically, extensive experiments on three benchmark KG and one Twitter dataset with various degrees of false negative/positive cases demonstrate the effectiveness of nPUGraph.</abstract>
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%0 Conference Proceedings
%T Noisy Positive-Unlabeled Learning with Self-Training for Speculative Knowledge Graph Reasoning
%A Wang, Ruijie
%A Li, Baoyu
%A Lu, Yichen
%A Sun, Dachun
%A Li, Jinning
%A Yan, Yuchen
%A Liu, Shengzhong
%A Tong, Hanghang
%A Abdelzaher, Tarek
%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 wang-etal-2023-noisy
%X This paper studies speculative reasoning task on real-world knowledge graphs (KG) that contain both false negative issue (i.e., potential true facts being excluded) and false positive issue (i.e., unreliable or outdated facts being included). State-of-the-art methods fall short in the speculative reasoning ability, as they assume the correctness of a fact is solely determined by its presence in KG, making them vulnerable to false negative/positive issues. The new reasoning task is formulated as a noisy Positive-Unlabeled learning problem. We propose a variational framework, namely nPUGraph, that jointly estimates the correctness of both collected and uncollected facts (which we call label posterior) and updates model parameters during training. The label posterior estimation facilitates speculative reasoning from two perspectives. First, it improves the robustness of a label posterior-aware graph encoder against false positive links. Second, it identifies missing facts to provide high-quality grounds of reasoning. They are unified in a simple yet effective self-training procedure. Empirically, extensive experiments on three benchmark KG and one Twitter dataset with various degrees of false negative/positive cases demonstrate the effectiveness of nPUGraph.
%R 10.18653/v1/2023.findings-acl.153
%U https://aclanthology.org/2023.findings-acl.153
%U https://doi.org/10.18653/v1/2023.findings-acl.153
%P 2440-2457
Markdown (Informal)
[Noisy Positive-Unlabeled Learning with Self-Training for Speculative Knowledge Graph Reasoning](https://aclanthology.org/2023.findings-acl.153) (Wang et al., Findings 2023)
ACL
- Ruijie Wang, Baoyu Li, Yichen Lu, Dachun Sun, Jinning Li, Yuchen Yan, Shengzhong Liu, Hanghang Tong, and Tarek Abdelzaher. 2023. Noisy Positive-Unlabeled Learning with Self-Training for Speculative Knowledge Graph Reasoning. In Findings of the Association for Computational Linguistics: ACL 2023, pages 2440–2457, Toronto, Canada. Association for Computational Linguistics.