Few-Shot Relation Prediction of Knowledge Graph via Convolutional Neural Network with Self-Attention

Shanna Zhong, Jiahui Wang, Kun Yue, Liang Duan, Zhengbao Sun, Yan Fang

Abstract: Knowledge graph (KG) has become the vital resource for various applications like question answering and recommendation system. However, several relations in KG only have few observed triples, which makes it necessary to develop the method for few-shot relation prediction. In this paper, we propose the Convolutional Neural Network with Self-Attention Relation Prediction (CARP) model to predict new facts with few observed triples. First, to learn the relation property features, we build a feature encoder by using the convolutional neural network with self-attention from the few observed triples rather than background knowledge. Then, by incorporating the learned features, we give an embedding network to learn the representation of incomplete triples. Finally, we give the loss function and training algorithm of our CARP model. Experimental results on three real-world datasets show that our proposed method improves Hits@10 by 48% on average over the state-of-the-art competitors.