郑州大学学报（工学版）2024，Vol.45Issue(4)：53-61,9.DOI:10.13705/j.issn.1671-6833.2024.01.016
基于关系学习的小样本知识图谱补全模型
Relation Learning Completion Model for Few-shot Knowledge Graphs
摘要
Abstract
In few-show knowledge graphs,the representation of relationships between entity pairs was diverse and complex.However,existing few-show knowledge graph completion methods commonly suffered from insufficient re-lational learning capabilities and the neglect of contextual semantics associated with entities.To address these chal-lenges,a novel approach called the few-shot relation learning completion model(FRLC)was proposed.Firstly,during the process of aggregating high-order neighborhood entity information,a gating mechanism was introduced to mitigate the adverse effects of noise on neighbors while enriching the representation of central entities.Secondly,in the phase of relation representation learning,the correlations among entity pairs in a reference set were leveraged to obtain more accurate relationship representations.Lastly,within the Transformer-based learning framework,an LSTM structure was incorporated to further capture contextual semantic information of entities and relationships,which was used for predicting new factual knowledge.To validate the effectiveness of FRLC,comparative experi-ments were conducted on the publicly available NELL-One and Wiki-One datasets,in which FRLC was compared with six few-shot knowledge graph completion models and five traditional models for 5-shot link prediction.The ex-perimental results showed improvements in FRLC across four metrics:MRR,Hits@10,Hits@5,and Hits@1,demonstrating the model's effectiveness.关键词
知识图谱补全/小样本关系/邻域聚合/链接预测Key words
knowledge graph completion/few-shot relation/neighborhood aggregation/link prediction分类
信息技术与安全科学引用本文复制引用
李卫军,顾建来,张新勇,高庾潇,刘锦彤..基于关系学习的小样本知识图谱补全模型[J].郑州大学学报（工学版）,2024,45(4):53-61,9.基金项目
中央高校基本科研业务费专项资金资助项目(2021JCYJ12) （2021JCYJ12）
国家自然科学基金资助项目(62066038,61962001) （62066038,61962001）
宁夏自然科学基金资助项目(2021AAC03215) （2021AAC03215）
