计算机应用研究2026，Vol.43Issue(6)：1692-1699,8.DOI:10.19734/j.issn.1001-3695.2025.09.0422
基于关系平滑去噪与神经过程的小样本知识图谱补全方法
Few-shot knowledge graph completion method based on relation-aware smoothing denoising and neural processes
摘要
Abstract
In order to solve the issues of noise interference and inadequate OOD generalization in FKGC,this proposed a relation-aware smoothing denoising neural processes framework,consisting of two modules:relation-aware smoothing denoising and uncertainty-aware generalization modeling.Firstly,it applied semantic smoothing to reduce bias caused by incorrect labels,and pruned false connections based on edge confidence to optimize the structure.To enhance generalization on complex patterns and anomalous data,it used a neural processes combined with normalized flows and stochastic decoders to model and strengthen the functional distribution of relations.Experiments conducted on three public benchmark datasets,NELL,WIKI,and FB15K-237.It demonstrates that the proposed algorithm outperforms ten other algorithms,achieving significant improve-ments of 7.1,4.1,and 5.1 percentage points in MRR,and 1.1,3.2,and 6.7 percentage points in hits@10,respectively.The experimental results confirm that the proposed framework achieves higher prediction accuracy and stronger generalization capability,validating its effectiveness in FKGC.关键词
小样本学习/知识图谱补全/关系平滑去噪/神经过程Key words
few-shot learning/knowledge graph completion/relation-aware smoothing denoising/neural processes分类
信息技术与安全科学引用本文复制引用
李海超,关常来,康生国,张宜成,秦继伟..基于关系平滑去噪与神经过程的小样本知识图谱补全方法[J].计算机应用研究,2026,43(6):1692-1699,8.基金项目
国家自然科学基金资助项目(202404120007) （202404120007）
企业横向合作项目(202504140005) （202504140005）
