Cross-relational attention mechanism-driven graph neural network and Apriori algorithm are used for knowledge reasoning in knowledge graphs

Knowledge reasoning for knowledge graphs refers to predicting unknown re-lationships in knowledge graphs to achieve automatic completion and expan-sion of knowledge. For the task of link prediction in knowledge graphs, this paper proposes an improved algorithm for link prediction in knowledge graphs based on relational graph neural networks and cross-relation attention mechanism, and integrates the Apriori association rule mining algorithm. The cross-relation attention mechanism enables information transfer across mul-tiple relationships between nodes, improving the performance of graph neu-ral networks. Using the Apriori association rule mining algorithm for data preprocessing can greatly filter out useless information in the inference in-put, improving the quality of the inference results. Finally, this model was compared with GCN, GAT, and R-GCN on two datasets, FB15K-237 and WN18, and the effectiveness of the proposed method was demonstrated. When training with a subgraph size of 80,000, the model achieves an MRR of 0.2753 on the FB15K-237 dataset and 0.9054 on the WN18 dataset.