Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (1): 310-315.doi: 10.11896/jsjkx.230300006

• Artificial Intelligence • Previous Articles     Next Articles

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

HU Binhao1,2, ZHANG Jianpeng2, CHEN Hongchang2   

  1. 1 School of Cyber Science and Engineering,Zhengzhou University,Zhengzhou 450002,China
    2 National Digital Switching System Engineering & Technological R&D Center(NDSC),Institute of Information Technology,University of Information Engineering,Zhengzhou 450002,China
  • Received:2023-03-01 Revised:2023-06-13 Online:2024-01-15 Published:2024-01-12
  • About author:HU Binhao,born in 1996,postgraduate.His main research interests include graph representation,knowledge graph and natural language processing.
    ZHANG Jianpeng,born in 1988,Ph.D,assistant researcher.His main research interest is big data analysis.
  • Supported by:
    National Natural Science Foundation of China(62002384) and Song Shan Laboratory(221100210700-3).

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Abstract: 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.

Key words: Knowledge graph completion, Generative adversarial network, Positive unlabeled learning, Negative sampling

CLC Number: 

  • TP391.1
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