Data Augmentation for Few-Shot Knowledge Graph Completion from Hierarchical Perspective

Yuanzhou Yao, Zhao Zhang, Yongjun Xu, Chao Li


Abstract
Few-shot knowledge graph completion (FKGC) has become a new research focus in the field of knowledge graphs in recent years, which aims to predict the missing links for relations that only have a few associative triples. Existing models attempt to solve the problem via learning entity and relation representations. However, the limited training data severely hinders the performance of existing models. To this end, we propose to solve the FKGC problem with the data augmentation technique. Specifically, we perform data augmentation from two perspectives, i.e., inter-task view and intra-task view. The former generates new tasks for FKGC, while the latter enriches the support or query set for an individual task. It is worth noting that the proposed framework can be applied to a number of existing FKGC models. Experimental evaluation on two public datasets indicates our model is capable of achieving substantial improvements over baselines.
Anthology ID:
2022.coling-1.220
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
2494–2503
Language:
URL:
https://aclanthology.org/2022.coling-1.220
DOI:
Bibkey:
Cite (ACL):
Yuanzhou Yao, Zhao Zhang, Yongjun Xu, and Chao Li. 2022. Data Augmentation for Few-Shot Knowledge Graph Completion from Hierarchical Perspective. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2494–2503, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Data Augmentation for Few-Shot Knowledge Graph Completion from Hierarchical Perspective (Yao et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.220.pdf
Data
Wiki-One