计算机科学与探索2024，Vol.18Issue(3)：646-658,13.DOI:10.3778/j.issn.1673-9418.2212076
融合选择注意力的小样本知识图谱补全模型
Few-Shot Knowledge Graph Completion Based on Selective Attention
摘要
Abstract
Most few-shot knowledge graph completion models have some problems,such as low ability to learn rela-tion representation and rarely attaching importance to the relative location and interaction between query entity pair when the relation between entities is complex or triples'neighborhood is sparse.A selective attention mechanism and interaction awareness(SAIA)based few-shot knowledge graph completion algorithm is proposed.Firstly,by in-troducing selective attention mechanism in the process of aggregating neighbor information,the neighbor encoder pays more attention to important neighbors to reduce adverse effects of noise neighbors.Secondly,SAIA utilizes the information related to task relation in the background knowledge graph to learn more accurate relation embedding in the process of relationship representation learning.Finally,in order to mine the interaction information and location information between entities in knowledge graph,a common interaction rate index(CIR)of entity pair is designed to measure the degree of association between entities in 3-hop path.Then,SAIA combines entity pair semantic infor-mation to predict new fact.Experimental results show that SAIA outperforms the state-of-the-art few-shot knowl-edge graph completion methods.Compared with the optimal results of baseline models,the proposed method achieves 5-shot link prediction performance improvement of 0.038,0.011,0.028 and 0.052 on NELL-one dataset and 0.034,0.037,0.029 and 0.027 on Wiki-one dataset by the metric MRR,Hits@10,Hits@5 as well as Hits@1,which verifies the effectiveness and feasibility of SAIA.关键词
知识图谱/知识图谱补全/表示学习/小样本关系/注意力机制Key words
knowledge graph/knowledge graph completion/representation learning/few-shot relation/attention mechanism分类
信息技术与安全科学引用本文复制引用
林穗,卢超海,姜文超,林晓珊,周蔚林..融合选择注意力的小样本知识图谱补全模型[J].计算机科学与探索,2024,18(3):646-658,13.基金项目
国家自然科学基金重点项目(62237001) （62237001）
广东省科技计划项目(2019B010139001) （2019B010139001）
广东省自然科学基金项目(2021A1515011243).This work was supported by the Key Project of National Natural Science Foundation of China(62237001),the Science and Technology Plan Project of Guangdong Province(2019B010139001),and the Natural Science Foundation of Guangdong Province(2021A1515011243). （2021A1515011243）
