计算机科学 ›› 2020, Vol. 47 ›› Issue (4): 189-193.doi: 10.11896/jsjkx.190300024

• 人工智能 • 上一篇    下一篇

一种基于改进向量投影距离的知识图谱表示方法

李鑫超, 李培峰, 朱巧明   

  1. 苏州大学计算机科学与技术学院 江苏 苏州215006江苏省计算机信息处理技术重点实验室 江苏 苏州215006
  • 收稿日期:2019-03-08 出版日期:2020-04-15 发布日期:2020-04-15
  • 通讯作者: 李培峰(pfli@suda.edu.cn)
  • 基金资助:
    国家自然科学基金项目(61836007,61772354,61773276)

Knowledge Graph Representation Based on Improved Vector Projection Distance

LI Xin-chao, LI Pei-feng, ZHU Qiao-ming   

  1. School of Computer Science and Technology,Soochow University,Suzhou,Jiangsu 215006,ChinaProvincial Key Laboratory for Computer Information Processing Technology,Suzhou,Jiangsu 215006,China
  • Received:2019-03-08 Online:2020-04-15 Published:2020-04-15
  • Contact: LI Pei-feng,born in 1971,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include natural language processing and machine learning.
  • About author:LI Xin-chao,born in 1995,postgradua-te.His main research interests include natural language processing and representation learning.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China(61836007,61772354,61773276).

摘要: 表示学习在知识图谱推理中有着重要的研究价值,将知识库中的实体和关系用连续低维向量进行表示,可实现知识的可计算。基于向量投影距离的知识表示学习模型在面对复杂关系时有较好的知识表达能力,但在处理一对一简单关系时容易受到无关信息的干扰,并且在一对多、多对一和多对多等复杂关系上存在性能提升空间。为此,文中提出了一个基于改进向量投影距离的知识表示学习模型SProjE,该模型引入自适应度量方法,降低了噪声信息的影响。在此基础上,通过进一步优化损失函数来提高复杂关系三元组的损失权重。该模型适用于大规模知识图谱的表示学习任务。最后,在标准知识图谱数据集WN18和FB15K上分析和验证了所提方法的有效性,基于链路预测任务的评测实验结果表明,相较于现有的模型和方法,SProjE在各项性能指标上均取得了明显的进步。

关键词: 知识图谱, 表示学习, 自适应度量, 链路预测

Abstract: Representation learning is of great value in knowledge graph reasoning,which realizes the computability of knowledge by embedding entities and relationships into a low-dimensional space.The representation learning model based on vector projection distance has better ability of knowledge representation on complex relationships.However,the model is easily susceptible to irrelevant information,especially when dealing with one-to-one relationships,and it still has space to improve performance in representing one-to-many,many-to-one and many-to-many relationships.In this paper,we proposed an improved representation learning model SProjE,which introduces an adaptive metric method to reduce the weight of noise information and optimizes the loss function to improve the loss weight of complex relation triples.The proposed model is suitable for large scale knowledge graph representation learning.At last,the experimental results on the WN18 and FB15k data sets show that SProjE achieves significant and consistent improvements compared with the existing models and methods.

Key words: Knowledge graph, Representation learning, Adaptive metric, Entity link prediction

中图分类号: 

  • TP391.1
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