ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2020, Vol. 57 ›› Issue (12): 2514-2522.doi: 10.7544/issn1000-1239.2020.20200741

所属专题: 2020人机混合增强智能的典型应用专题

• 人工智能 • 上一篇    下一篇

规则引导的知识图谱联合嵌入方法

姚思雨1,2,赵天哲1,2,王瑞杰1,2,3,刘均1,2   

  1. 1(西安交通大学计算机科学与技术学院 西安 710049);2(陕西省天地网技术重点实验室(西安交通大学) 西安 710049);3(苏黎世大学计算机科学系 瑞士 8050) (cheryl@stu.xjtu.edu.cn)
  • 出版日期: 2020-12-01
  • 基金资助: 
    国家重点研发计划项目(2018YFB1004500);国家自然科学基金项目(61672419,61672418,61877050,61937001);中国工程院咨询研究项目;国家自然科学基金创新研究群体(61721002);教育部创新团队(IRT_17R86);中国工程科技知识中心项目

Rule-Guided Joint Embedding Learning of Knowledge Graphs

Yao Siyu1,2, Zhao Tianzhe1,2, Wang Ruijie1,2,3, Liu Jun1,2   

  1. 1(College of Computer Science and Technology, Xi’an Jiaotong University, Xi’an 710049);2(Shaanxi Key Laboratory of STN Technology R&D (Xi’an Jiaotong University), Xi’an 710049);3(Department of Informatics, University of Zurich, Switzerland 8050)
  • Online: 2020-12-01
  • Supported by: 
    This work was supported by the National Key Research and Development Program of China (2018YFB1004500), the National Natural Science Foundation of China (61672419, 61672418, 61877050, 61937001), the Consulting Research Project of Chinese Academy of Engineering, the Innovative Research Group Project of the National Natural Science Foundation of China (61721002), the Ministry of Education Innovation Research Team (IRT_17R86), and the Project of China Knowledge Centre for Engineering Science and Technology.

摘要: 近年来,大量研究工作致力于知识图谱的嵌入学习,旨在将知识图谱中的实体与关系映射到低维连续的向量空间中.且所学习到的嵌入表示已被成功用于缓解大规模知识图谱的计算效率低下问题.然而,大多数现有嵌入学习模型仅考虑知识图谱的结构信息.知识图谱中还包含有丰富的上下文信息和文本信息,它们也可被用于学习更准确的嵌入表示.针对这一问题,提出了一种规则引导的知识图谱联合嵌入学习模型,基于图卷积网络,将上下文信息与文本信息融合到实体与关系的嵌入表示中.特别是针对上下文信息的卷积编码,通过计算单条上下文信息的置信度与关联度来度量其重要程度.对于置信度,定义了一个简单有效的规则并依据该规则进行计算.对于关联度,提出了一种基于文本表示的计算方法.最后,在2个基准数据集上进行的实验结果证明了模型的有效性.

关键词: 知识图谱, 表示学习, 图卷积网络, 上下文信息, 文本信息

Abstract: In recent years, numerous research works have been devoted to knowledge graph embedding learning which aims to encode entities and relations of the knowledge graph in continuous low-dimensional vector spaces. And the learned embedding representations have been successfully utilized to alleviate the computational inefficiency problem of large-scale knowledge graphs. However, most existing embedding models only consider the structural information of the knowledge graph. The contextual information and literal information are also abundantly contained in knowledge graphs and could be exploited to learn better embedding representations. In this paper, we focus on this problem and propose a rule-guided joint embedding learning model which integrates the contextual information and literal information into the embedding representations of entities and relations based on graph convolutional networks. Especially for the convolutional encoding of the contextual information, we measure the importance of a piece of contextual information by computing its confidence and relatedness metrics. For the confidence metric, we define a simple and effective rule and propose a rule-guided computing method. For the relatedness metric, we propose a computing method based on the representations of the literal information. We conduct extensive experiments on two benchmark datasets, and the experimental results demonstrate the effectiveness of the proposed model.

Key words: knowledge graph, representation learning, graph convolutional networks, contextual information, literal information

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