CAKE: A Scalable Commonsense-Aware Framework For Multi-View Knowledge Graph Completion

Guanglin Niu, Bo Li, Yongfei Zhang, Shiliang Pu


Abstract
Knowledge graphs store a large number of factual triples while they are still incomplete, inevitably. The previous knowledge graph completion (KGC) models predict missing links between entities merely relying on fact-view data, ignoring the valuable commonsense knowledge. The previous knowledge graph embedding (KGE) techniques suffer from invalid negative sampling and the uncertainty of fact-view link prediction, limiting KGC’s performance. To address the above challenges, we propose a novel and scalable Commonsense-Aware Knowledge Embedding (CAKE) framework to automatically extract commonsense from factual triples with entity concepts. The generated commonsense augments effective self-supervision to facilitate both high-quality negative sampling (NS) and joint commonsense and fact-view link prediction. Experimental results on the KGC task demonstrate that assembling our framework could enhance the performance of the original KGE models, and the proposed commonsense-aware NS module is superior to other NS techniques. Besides, our proposed framework could be easily adaptive to various KGE models and explain the predicted results.
Anthology ID:
2022.acl-long.205
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2867–2877
Language:
URL:
https://aclanthology.org/2022.acl-long.205
DOI:
10.18653/v1/2022.acl-long.205
Bibkey:
Cite (ACL):
Guanglin Niu, Bo Li, Yongfei Zhang, and Shiliang Pu. 2022. CAKE: A Scalable Commonsense-Aware Framework For Multi-View Knowledge Graph Completion. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2867–2877, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
CAKE: A Scalable Commonsense-Aware Framework For Multi-View Knowledge Graph Completion (Niu et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.205.pdf
Code
 ngl567/cake
Data
ConceptNetFB15k-237NELL-995