RIECN: learning relation-based interactive embedding convolutional network for knowledge graph

Most knowledge graphs(KGs) are large and incomplete graph-structure database, which can be completed by predicting miss links according to the existing knowledge. The mainstream method is knowledge graph embedding (KGE) which is designed to learn low dimensional embedding of entities and relations. However, knowledge graph embedding still faces two major issues: (1) How to generate more expressive embeddings? (2) How to solve semantic polysemy of entities in different relations? In this paper, we propose a novel KG embedding model, RIECN (Relation-based Interactive Embedding Convolutional Network), which achieves high-quality performance and shows some advancements in modeling complex relations. In RIECN, FIR (Feature Interaction Reshaping) method is introduced to increase the feature interactions between entity and relation embeddings to generate more expressive feature maps. In addition, a new method of generating relation-based dynamic convolution filters, RDCF, is proposed. RDCF generates specific relation and hybird-size convolution filters, which enriches the feature maps of each entity improving the accuracy of link prediction task especially in complex relations scenario. We tested the performance of our model on five benchmark datasets. The experimental results show that the RIECN model significantly outperforms recent state-of-the-art models by 0.1-3.2% and 1.1-3.7%, in terms of MMR metric and Hit@1 metric, respectively.