Non-translational Alignment for Multi-relational Networks

Non-translational Alignment for Multi-relational Networks

Shengnan Li, Xin Li, Rui Ye, Mingzhong Wang, Haiping Su, Yingzi Ou

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Main track. Pages 4180-4186. https://doi.org/10.24963/ijcai.2018/581

Most existing solutions for the alignment of multi-relational networks, such as multi-lingual knowledge bases, are ``translation''-based which facilitate the network embedding via the trans-family, such as TransE. However, they cannot address triangular or other structural properties effectively. Thus, we propose a non-translational approach, which aims to utilize a probabilistic model to offer more robust solutions to the alignment task, by exploring the structural properties as well as leveraging on anchors to project each network onto the same vector space during the process of learning the representation of individual networks. The extensive experiments on four multi-lingual knowledge graphs demonstrate the effectiveness and robustness of the proposed method over a set of state-of-the-art alignment methods.
Keywords:
Machine Learning: Knowledge-based Learning
Natural Language Processing: Embeddings