Considering Sub-graph Structure in Representation Learning

Abstract

Representation learning aims to encode knowledge graphs into a low-dimensional vector space. However, since both the out-degree and in-degree of the entities in the knowledge graph follow the power-law distribution, only a small number of entities with higher frequency play a key role in training process, while the others have less effect. This leads to a more serious problem of data sparsity. In this paper, we propose two knowledge graph representation learning models for sub-graph structure fusion. Based on the original model, our models are trained by incorporating the sub-graph structure during training process. By incorporating the sub-graph information into the representation learning model, the structural associations between different relations can be incorporated while considering the semantic association between entities. So that the entities and relations can be modeled more accurately, and the data sparsity problem of the knowledge graph representation learning model can be alleviated effectively.