A Structural Representation Learning for Multi-relational Networks

A Structural Representation Learning for Multi-relational Networks

Lin Liu, Xin Li, William K. Cheung, Chengcheng Xu

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Main track. Pages 4047-4053. https://doi.org/10.24963/ijcai.2017/565

Most of the existing multi-relational network embedding methods, e.g., TransE, are formulated to preserve pair-wise connectivity structures in the networks. With the observations that significant triangular connectivity structures and parallelogram connectivity structures found in many real multi-relational networks are often ignored and that a hard-constraint commonly adopted by most of the network embedding methods is inaccurate by design, we propose a novel representation learning model for multi-relational networks which can alleviate both fundamental limitations. Scalable learning algorithms are derived using the stochastic gradient descent algorithm and negative sampling. Extensive experiments on real multi-relational network datasets of WordNet and Freebase demonstrate the efficacy of the proposed model when compared with the state-of-the-art embedding methods.
Keywords:
Natural Language Processing: Information Extraction
Natural Language Processing: Information Retrieval
Machine Learning: Multi-instance/Multi-label/Multi-view learning
Machine Learning: Knowledge-based Learning