Knowledge Graph Reasoning for Few-Shot Problems

This papermainly explores how to improve the performance of knowledge graph reasoning in natural language processing, especially in few-shot learning scenarios, by utilizing Graph Convolutional Networks (GCN), the meta-learning algorithm Reptile, and Reinforcement Learning. In this work, the study proposed a novel model, REGCKG, which combines the advantages of GCN, meta-learning, and reinforcement learning to effectively handle relationships with a small number of samples in knowledge graphs. REGCKG uses GCN to encode path information and utilizes Reptile to learn meta-parameters from high-frequency relationships, and then uses the meta-parameters to adapt to few-shot tasks, improving the model's generalization ability on few-shot data.