CoRI: Collective Relation Integration with Data Augmentation for Open Information Extraction

Zhengbao Jiang, Jialong Han, Bunyamin Sisman, Xin Luna Dong


Abstract
Integrating extracted knowledge from the Web to knowledge graphs (KGs) can facilitate tasks like question answering. We study relation integration that aims to align free-text relations in subject-relation-object extractions to relations in a target KG. To address the challenge that free-text relations are ambiguous, previous methods exploit neighbor entities and relations for additional context. However, the predictions are made independently, which can be mutually inconsistent. We propose a two-stage Collective Relation Integration (CoRI) model, where the first stage independently makes candidate predictions, and the second stage employs a collective model that accesses all candidate predictions to make globally coherent predictions. We further improve the collective model with augmented data from the portion of the target KG that is otherwise unused. Experiment results on two datasets show that CoRI can significantly outperform the baselines, improving AUC from .677 to .748 and from .716 to .780, respectively.
Anthology ID:
2021.acl-long.363
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
August
Year:
2021
Address:
Online
Editors:
Chengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4706–4716
Language:
URL:
https://aclanthology.org/2021.acl-long.363
DOI:
10.18653/v1/2021.acl-long.363
Bibkey:
Cite (ACL):
Zhengbao Jiang, Jialong Han, Bunyamin Sisman, and Xin Luna Dong. 2021. CoRI: Collective Relation Integration with Data Augmentation for Open Information Extraction. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 4706–4716, Online. Association for Computational Linguistics.
Cite (Informal):
CoRI: Collective Relation Integration with Data Augmentation for Open Information Extraction (Jiang et al., ACL-IJCNLP 2021)
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PDF:
https://aclanthology.org/2021.acl-long.363.pdf
Video:
 https://aclanthology.org/2021.acl-long.363.mp4