Multi-Channel Graph Neural Network for Entity Alignment
Yixin Cao, Zhiyuan Liu, Chengjiang Li, Zhiyuan Liu, Juanzi Li, Tat-Seng Chua
Abstract
Entity alignment typically suffers from the issues of structural heterogeneity and limited seed alignments. In this paper, we propose a novel Multi-channel Graph Neural Network model (MuGNN) to learn alignment-oriented knowledge graph (KG) embeddings by robustly encoding two KGs via multiple channels. Each channel encodes KGs via different relation weighting schemes with respect to self-attention towards KG completion and cross-KG attention for pruning exclusive entities respectively, which are further combined via pooling techniques. Moreover, we also infer and transfer rule knowledge for completing two KGs consistently. MuGNN is expected to reconcile the structural differences of two KGs, and thus make better use of seed alignments. Extensive experiments on five publicly available datasets demonstrate our superior performance (5% Hits@1 up on average). Source code and data used in the experiments can be accessed at https://github.com/thunlp/MuGNN .- Anthology ID:
- P19-1140
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1452–1461
- Language:
- URL:
- https://aclanthology.org/P19-1140
- DOI:
- 10.18653/v1/P19-1140
- Cite (ACL):
- Yixin Cao, Zhiyuan Liu, Chengjiang Li, Zhiyuan Liu, Juanzi Li, and Tat-Seng Chua. 2019. Multi-Channel Graph Neural Network for Entity Alignment. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1452–1461, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Multi-Channel Graph Neural Network for Entity Alignment (Cao et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/P19-1140.pdf
- Code
- thunlp/MuGNN
- Data
- DBP15K