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
- Editors:
- Smaranda Muresan, Preslav Nakov, Aline Villavicencio
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2022.acl-long.205.pdf
- Code
- ngl567/cake
- Data
- ConceptNet, FB15k-237, NELL-995