DcKE: A Dual Encoder-decoder Knowledge Embedding Model for Link Prediction

Knowledge Graph Embedding (KGE) is a powerful technique for predicting missing links in knowledge graphs. Current mainstream research primarily focuses on Transformer-based language pre-training models and graph neural network models. However, these models suffer from several issues, including a large number of parameters, low training efficiency, and dimensional explosion, which hinder their application in large-scale knowledge graphs. This paper explores the impact of entities and relations embedding dimensions on the interaction between them. Through experimental analysis, we examine how these dimensions affect the performance of KGE models. Based on these insights, we propose a novel dual encoder-decoder model called DcKE, which is designed to capture long-distance interactions and improve link prediction performance with less parameters. We conduct an extensive experimental evaluation on four widely-used datasets: WN18RR, FB15k-237, DB100k, and YAGO3-10. The results demonstrate that DcKE, by utilizing a small number of parameters and dimensions, significantly improves model efficiency, making it a promising method for large-scale knowledge graph embedding.