Relational reasoning based on deep reinforcement learning

Large knowledge graphs always have the problem of missing relationships or entities in triples, and missing this information can lead to inference failure. To solve this problem, this paper proposes a knowledge graph inference method based on deep reinforcement learning. This method uses the idea of combining reinforcement learning with meta learning contrastive learning to complete the triplet information of the knowledge graph, and optimizes the reward function to improve the model performance. Comparative experiments were conducted on the NELL-995 and FB15K-237 datasets, and the experimental results showed that the proposed model outperformed most previous models.