计算机工程2025，Vol.51Issue(5)：124-132,9.DOI:10.19678/j.issn.1000-3428.0069130
基于图神经网络与元学习的小样本关系推理模型
Few-shot Relation Reasoning Model Based on Graph Neural Network and Meta-Learning
摘要
Abstract
Knowledge graph relation reasoning aims to infer missing links between entities.Current knowledge graph relation reasoning models perform poorly when reasoning with few-shot relations and struggle to reason for entities that are not seen during training.To address these issues,this study proposes a knowledge graph few-shot relation inductive reasoning model based on Graph Neural Network(GNN)and loss-weight meta-learning.The model utilizes GNN to learn the subgraph patterns between target entities,thereby generalizing to relation reasoning for new entities.The path-wise masking strategy reduces the reliance of the model on specific subgraph patterns and captures the key features and patterns in the data,thereby enhancing the inductive reasoning and generalization capabilities of the model.A distribution-balanced meta-dataset based on meta-learning is introduced to learn the adaptive loss function.The loss weights of different samples are adjusted,enabling the model to focus more on challenging few-shot relations,thereby improving the accuracy of the few-shot relation prediction.Finally,triplets without subgraphs in inductive link prediction benchmark datasets FB15k-237 and NELL-995 are filtered out,and link prediction and triplet classification tasks are performed.Simultaneously,triplets belonging to few-shot relations in the test set are evaluated.Experimental results show that the proposed model exhibits the best performance on the inductive reasoning benchmark datasets and achieves an average improvement of approximately 1％over current state-of-the-art models on seven few-shot datasets.关键词
知识图谱/图神经网络/小样本关系预测/路径掩码/损失权重元学习Key words
knowledge graph/Graph Neural Network(GNN)/few-shot relation prediction/path-wise masking/loss-weight meta-learning分类
计算机与自动化引用本文复制引用
刘文杰,陈亮,任智杰..基于图神经网络与元学习的小样本关系推理模型[J].计算机工程,2025,51(5):124-132,9.基金项目
国家自然科学基金(62071240) （62071240）
江苏省自然科学基金(BK20231142). （BK20231142）
