Few-Shot Knowledge Graph Completion based on Data Enhancement

Knowledge graphs (KGs) are widely used in various natural language processing applications. In order to expand the coverage of a KG, KG completion has attracted extensive attention. The commonly used embedding methods based on a large amount of training data can play an important role in this work. However, with few of triples, the performance of these methods will be greatly reduced. The completion of this kind of few-shot task is more challenging. In this work, we propose a method of data enhancement to increase the data quantity and solve the problem of sample shortage. Specifically, we first observe that the representation vectors of the relation in a KG are approximately subordinate to Gaussian distribution. Then we construct a Gaussian distribution for the relation of each triple in few-shot task according to the distributions of its similar relations in background graph. Further, we sample from the Gaussian distribution of each triple to expand the training data. Finally, we use an adaptive attentional network model FAAN proposed by Sheng et al. as the baseline model. Experimental results on two public datasets NELL-One and Wiki-One show that the proposed method achieves better performance.