计算机科学 ›› 2023, Vol. 50 ›› Issue (4): 149-158.doi: 10.11896/jsjkx.211200175

• 人工智能 • 上一篇    下一篇

知识图谱嵌入模型中的损失函数研究综述

申秋慧1, 张宏军2, 徐有为1, 王航1, 程恺2   

  1. 1 陆军工程大学研究生院 南京 210007
    2 陆军工程大学指挥控制工程学院 南京 210000
  • 收稿日期:2021-12-15 修回日期:2022-09-05 出版日期:2023-04-15 发布日期:2023-04-06
  • 通讯作者: 张宏军(jsnjzhj_lgdx@163.com)
  • 作者简介:(maple_dancing@zknu.edu.cn)
  • 基金资助:
    国家自然科学基金(61806221)

Comprehensive Survey of Loss Functions in Knowledge Graph Embedding Models

SHEN Qiuhui1, ZHANG Hongjun2, XU Youwei1, WANG Hang1, CHENG Kai2   

  1. 1 School of Graduate,Army Engineering University of PLA,Nanjing 210007,China
    2 College of Command and Control Engineering,Army Engineering University of PLA,Nanjing 210000,China
  • Received:2021-12-15 Revised:2022-09-05 Online:2023-04-15 Published:2023-04-06
  • About author:SHEN Qiuhui,born in 1987,Ph.D,lecturer.Her main research interests include knowledge graph and cloud computing.
    ZHANG Hongjun,born in 1963,professor,Ph.D supervisor.His main research interests include data engineering and military modeling & simulation.
  • Supported by:
    National Natural Science Foundation of China(61806221).

摘要: 表达方式丰富直观的知识图谱得到了大量学者的关注。在知识图谱嵌入方面已积累了大量研究,其成果在电商、金融、医药、交通、智能问答等领域发挥了重要的作用。其中,损失函数在知识图谱嵌入模型的训练阶段起到了非常关键的作用。在现有知识图谱嵌入研究的基础上,根据基础损失函数把模型中使用的损失函数梳理为合页损失、逻辑回归损失、交叉熵损失、对数似然损失、负采样损失和均方误差损失六大类,并逐类详细分析了损失函数的原型公式、物理含义和其在知识图谱嵌入模型中的扩展、演变及应用。在此基础上,对静态和动态两大知识图谱场景中各种损失函数的使用情况、效率和收敛性进行了综合分析评价;根据分析结果,结合知识图谱的发展应用趋势和损失函数现状,对损失函数的未来研究方向进行了探讨。

关键词: 知识图谱, 损失函数, 嵌入模型, 知识表示学习

Abstract: Due to its rich and intuitive expressivity,knowledge graph has received much attention of many scholars. A lot of works have been accumulated in knowledge graph embedding. The results of the works have played an important role in some fields, such as e-commerce, finance,medicine, transportation and intelligent Q & A. In the knowledge graph embedding model, the loss function plays a key role in its training stage. Based on the existing research of knowledge graph embeddings, this paper classifies the loss functions used in the model into six categories: hinge loss, logistic loss, cross entropy loss, log likelihood loss, negative sampling loss and mean square error loss. The prototype formula and physical meaning of loss functions and their expansion, evolution and application in knowledge graph embedding models are analyzed in detail one by one.Based on the above,the usage, efficiency and convergence of various loss functions in the static and dynamic knowledge graph scenarios are comprehensively analyzed and evaluated. According to the analysis results, combined with the development and application trend of knowledge graph and the current situation of loss functions,the future works of loss functions are discussed.

Key words: Knowledge graph, Loss function, Embedding model, Knowledge representation learning

中图分类号: 

  • TP311
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