计算机工程与应用2024，Vol.60Issue(2)：113-120,8.DOI:10.3778/j.issn.1002-8331.2208-0339
k阶采样和图注意力网络的知识图谱表示模型
Knowledge Graph Embedding Model Based on k-Order Sampling and Graph Attention Networks
摘要
Abstract
Knowledge graph embedding(KGE)aims to map entities and relations of knowledge graph into a low-dimensional space to obtain its vector representation.Existing KGE models only consider the first-order neighbors,which influence the accuracy of reasoning and prediction tasks in knowledge graph.In order to solve this problem,a novel KGE model based on k-order sampling algorithm and graph attention networks is proposed.Firstly,a k-order sampling algorithm is proposed to obtain the neighbors'features of a central entity by aggregating k-order neighborhood in the pruned sub-graph.Then,the graph attention networks are introduced to learn the attention values of the central entity's neighbors,and the new entity embedding is obtained by the weighted sum of neighbors'features.Finally,the ConvKB is used as a decoder to analyze the global embedding property of a triple.Evaluation experiments on several datasets,WN18RR,FB15k-237,NELL-995,Kinship,reveal that the model performs better than the state-of-the-art models on the task of link prediction.Besides,the influence on the model hit rate while changing order k or sampling coefficient b has been discussed.关键词
知识图谱表示/k阶采样算法/图注意力网络/剪枝子图/链接预测Key words
knowledge graph embedding/k-order sampling algorithm/graph attention networks/pruned subgraph/link prediction分类
信息技术与安全科学引用本文复制引用
刘文杰,姚俊飞,陈亮..k阶采样和图注意力网络的知识图谱表示模型[J].计算机工程与应用,2024,60(2):113-120,8.基金项目
国家自然科学基金(62071240,61802175) （62071240,61802175）
江苏省高等学校重点学术项目建设(PAPD). （PAPD）
