计算机工程与应用2024，Vol.60Issue(2)：147-153,7.DOI:10.3778/j.issn.1002-8331.2210-0023
邻域信息分层感知的知识图谱补全方法
Knowledge Graph Completion Method Based on Neighborhood Hierarchical Perception
摘要
Abstract
Knowledge graph completion(KGC)is aiming at inferring the missing values of triples by using the existing knowledge of the knowledge graph.Recently,there are some studies showing that applying the graph convolution network(GCN)to the KGC task can improve the inference performance of the model.Currently,most GCN models have the prob-lems of treating the neighborhood information equally,ignoring the different contributions of neighboring entities to the central entity,and using simple linear transformation to update the relationship embedding.Aiming at these problems,a neighborhood aware hierarchical attention network,named NAHAT,is proposed.In order to improve the expression abili-ty of the model,NAHAT introduces entity feature information into relation updating,and aggregates entity and relation representation to enrich heterogeneous relation semantics.At the same time,NAHAT applies self-adversarial negative sample training to the loss calculation to train the model efficiently.Compared with composition-based multi-relational graph convolutional networks,the Hits@1 and Hits@10 metrics of the proposed model increases by 3%and 2.6%respec-tively on the FB15K-237 dataset,and 0.9%and 2.2%respectively on the WN18RR dataset.Experimental results demon-strate the effectiveness of the proposed model.关键词
知识图谱/知识表示学习/分层注意力机制/图神经网络Key words
knowledge graph/knowledge representation learning/hierarchical attention mechanism(HAT)/graph neural network(GNN)分类
信息技术与安全科学引用本文复制引用
梁梅霖,段友祥,昌伦杰,孙歧峰..邻域信息分层感知的知识图谱补全方法[J].计算机工程与应用,2024,60(2):147-153,7.基金项目
中央高校基本科研业务费专项资金资助项目(20CX05017A) （20CX05017A）
中石油重大科技项目(ZD2019-183-006). （ZD2019-183-006）
