基于生成式对抗网络和正类无标签学习的知识图谱补全算法  

Knowledge Graph Completion Algorithm Based on Generative Adversarial Network and Positiveand Unlabeled Learning

作　　者：胡斌皓 张建朋[2] 陈鸿昶[2] HU Binhao;ZHANG Jianpeng;CHEN Hongchang(School of Cyber Science and Engineering,Zhengzhou University,Zhengzhou 450002,China;National Digital Switching System Engineering&Technological R&D Center(NDSC),Institute of Information Technology,University of Information Engineering,Zhengzhou 450002,China)

机构地区：[1]郑州大学网络空间安全学院,郑州450002 [2]信息工程大学信息技术研究所国家数字交换系统工程技术研究中心,郑州450002

出　　处：《计算机科学》2024年第1期310-315,共6页Computer Science

基　　金：国家自然科学基金(62002384);嵩山实验室项目(221100210700-3)。

摘　　要：随着知识图谱的应用越来越广泛,绝大多数真实世界的知识图谱通常具有不完备性,限制了知识图谱的实际应用效果。因此,知识图谱补全成为了知识图谱领域的热点。然而,现有方法大多集中在评分函数的设计上,少部分研究关注了负样本抽样策略。在改善负样本抽样的知识图谱补全算法的研究中,基于生成式对抗网络的方法取得了不错的进展。然而,现有研究并没有关注到负样本存在假阴性标签的问题,即生成的负样本中可能包含真实的事实。为了缓解假阴性标签问题,提出了一种基于生成式对抗网络和正类无标签学习的知识图谱补全算法。该方法利用生成式对抗网络生成无标签样本,并使用正类无标签学习缓解假阴性标签问题。在基准数据集上进行的大量实验证明了所提算法的有效性与准确性。With the widespread application of knowledge graphs,the majority of real-world knowledge graphs suffer from the problem of incompleteness,which hinders their practical applications.As a result,it makes knowledge graph completion become a hot topic in the field of knowledge graph.However,most existing methods focus on the design of scoring functions,with only a few studies paying attention to negative sampling strategies.In the research of knowledge graph completion algorithms which aims at improving negative sampling,the methods based on generative adversarial networks(GANs)have achieved significant progress.Nonetheless,existing studies have not addressed the false negative issue,meaning that generated negative samples may contain actual facts.To address this issue,this paper proposes a knowledge graph completion algorithm based on GAN and positive-unlabeled learning.In the proposed method,GANs are utilized to generate unlabeled samples,while positive unlabeled lear-ning is employed to alleviate the false negative problem.Extensive experiments on benchmark datasets demonstrate the effectiveness and accuracy of the proposed algorithm.

关 键 词：知识图谱补全 生成式对抗网络 正类无标签学习 负样本抽样 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]