A Renovated CNN-Based Model Enhances KGC Task Performance

Knowledge graph (KG) contains a large number of real-world knowledge and has become an invaluable aid to assist the application of artificial intelligence. Knowledge graph completion (KGC) is the task to complete the missing triple in KG database. Our goal in this study is to enhance the performance of KGC tasks based on CNN model. To do this, we first investigated the effect of adding multiple filters of different shapes into the pioneer model. The obscure improvement leads us to seek other approaches. Our second proposed model, termed DP-ConvKB, which is a deep convolution-neural-network-based model, outperforms state-of-the-art models on several metrics. Our study provides supporting evidence that, by cooperating deep pyramid network structure into models, it can significantly improve the KGC performances.