Tackling Long-Tailed Relations and Uncommon Entities in Knowledge Graph Completion

Zihao Wang, Kwunping Lai, Piji Li, Lidong Bing, Wai Lam


Abstract
For large-scale knowledge graphs (KGs), recent research has been focusing on the large proportion of infrequent relations which have been ignored by previous studies. For example few-shot learning paradigm for relations has been investigated. In this work, we further advocate that handling uncommon entities is inevitable when dealing with infrequent relations. Therefore, we propose a meta-learning framework that aims at handling infrequent relations with few-shot learning and uncommon entities by using textual descriptions. We design a novel model to better extract key information from textual descriptions. Besides, we also develop a novel generative model in our framework to enhance the performance by generating extra triplets during the training stage. Experiments are conducted on two datasets from real-world KGs, and the results show that our framework outperforms previous methods when dealing with infrequent relations and their accompanying uncommon entities.
Anthology ID:
D19-1024
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
250–260
Language:
URL:
https://aclanthology.org/D19-1024
DOI:
10.18653/v1/D19-1024
Bibkey:
Cite (ACL):
Zihao Wang, Kwunping Lai, Piji Li, Lidong Bing, and Wai Lam. 2019. Tackling Long-Tailed Relations and Uncommon Entities in Knowledge Graph Completion. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 250–260, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Tackling Long-Tailed Relations and Uncommon Entities in Knowledge Graph Completion (Wang et al., EMNLP 2019)
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PDF:
https://aclanthology.org/D19-1024.pdf
Attachment:
 D19-1024.Attachment.zip