Adaptive Attentional Network for Few-Shot Knowledge Graph Completion

Jiawei Sheng, Shu Guo, Zhenyu Chen, Juwei Yue, Lihong Wang, Tingwen Liu, Hongbo Xu


Abstract
Few-shot Knowledge Graph (KG) completion is a focus of current research, where each task aims at querying unseen facts of a relation given its few-shot reference entity pairs. Recent attempts solve this problem by learning static representations of entities and references, ignoring their dynamic properties, i.e., entities may exhibit diverse roles within task relations, and references may make different contributions to queries. This work proposes an adaptive attentional network for few-shot KG completion by learning adaptive entity and reference representations. Specifically, entities are modeled by an adaptive neighbor encoder to discern their task-oriented roles, while references are modeled by an adaptive query-aware aggregator to differentiate their contributions. Through the attention mechanism, both entities and references can capture their fine-grained semantic meanings, and thus render more expressive representations. This will be more predictive for knowledge acquisition in the few-shot scenario. Evaluation in link prediction on two public datasets shows that our approach achieves new state-of-the-art results with different few-shot sizes. The source code is available at https://github.com/JiaweiSheng/FAAN.
Anthology ID:
2020.emnlp-main.131
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1681–1691
Language:
URL:
https://aclanthology.org/2020.emnlp-main.131
DOI:
10.18653/v1/2020.emnlp-main.131
Bibkey:
Cite (ACL):
Jiawei Sheng, Shu Guo, Zhenyu Chen, Juwei Yue, Lihong Wang, Tingwen Liu, and Hongbo Xu. 2020. Adaptive Attentional Network for Few-Shot Knowledge Graph Completion. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1681–1691, Online. Association for Computational Linguistics.
Cite (Informal):
Adaptive Attentional Network for Few-Shot Knowledge Graph Completion (Sheng et al., EMNLP 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/update-css-js/2020.emnlp-main.131.pdf
Video:
 https://slideslive.com/38938679
Code
 JiaweiSheng/FAAN