Survey
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App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 4
961-967

Survey of knowledge representation learning methods

Zhang Zhenghanga,b
Qian Yuronga,b
Xing Yannia,b
Zhao Xina,b
a. College of Software, b. Key Laboratory of Signal Detection & Processing in Xinjiang Uygur Autonomous Region, Xinjiang University, Urumqi 830046, China
DOI: 10.19734/j.issn.1001-3695.2020.04.0094

Abstract

In recent years, knowledge representation learning has become a hot topic in the field of knowledge graph. In order to grasp the current research status of knowledge representation learning methods in time, this paper introduced and classified the representative knowledge representation methods through induction and sorting, which were mainly divided into traditional knowledge representation model, improved knowledge representation model and other knowledge representation models. This paper summarized and analyzed the problems, algorithm ideas, application scenarios, evaluation indicators, advantages and disadvantages of each method in detail. Through research, this paper found that the current knowledge representation learning mainly faces the challenges of relationship path modeling, accuracy and complex relationship processing. Aiming at these challenges, this paper looked forward to using semantic composition of relationship to represent path, using entity alignment evaluation index, modeling in entity space and relationship space, and using text context information to expand the solution of KG's semantic structure resolution.

Foundation Support

国家自然科学基金资助项目(61966035)
新疆维吾尔自治区智能多模态信息处理团队(XJEDU2017T002)
新疆维吾尔自治区研究生创新项目(XJ2019G072)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2020.04.0094
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 4
Section: Survey
Pages: 961-967
Serial Number: 1001-3695(2021)04-001-0961-07

Publish History

[2021-04-05] Printed Article

Cite This Article

张正航, 钱育蓉, 行艳妮, 等. 知识表示学习方法研究综述 [J]. 计算机应用研究, 2021, 38 (4): 961-967. (Zhang Zhenghang, Qian Yurong, Xing Yanni, et al. Survey of knowledge representation learning methods [J]. Application Research of Computers, 2021, 38 (4): 961-967. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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