Construction and Representation Learning of EAKG
CSTR:
Author:
Affiliation:

Clc Number:

Fund Project:

  • Article
  • |
  • Figures
  • |
  • Metrics
  • |
  • Reference
  • |
  • Related
  • |
  • Cited by
  • |
  • Materials
  • |
  • Comments
    Abstract:

    The Knowledge Graph is intended to describe the entities that exist in the real world and the relationships between entities. Since Google introduced the "Google Knowledge Graph" in 2012, knowledge graph have received widespread attention in academia and industry. Aiming at the lack of systematic organization in the field of education, the Educational Assessment Knowledge Graph (EAKG) for high schools is constructed. The construction of EAKG includes knowledge graph schema layer construction based on ontology technology and knowledge graph data layer construction based on schema layer structure. Compared with the traditional knowledge graph constructed by web crawling and other technical means, the knowledge graph constructed in this study has the advantages of clear logical structure and the description of the relationship between entities follows the definition of knowledge graph schema layer. EAKG provides good support for knowledge sharing, knowledge reasoning, knowledge representation learning and other tasks in the field. The experimental results on real simulated test data show that the EAKG constructed by introducing domain ontology as schema layer has better performance than EAKG constructed by data facts alone without domain ontology schema layer on the embedded representation learning tasks such as entity link prediction of test paper score prediction, knowledge point score prediction and triplet classification. Experiments show that the introduction of domain ontology has a certain guiding significance for knowledge graph representation learning.

    Reference
    Related
    Cited by
Get Citation

罗明.教育测评知识图谱的构建及其表示学习.计算机系统应用,2019,28(7):26-34

Copy
Share
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
History
  • Received:January 12,2019
  • Revised:February 03,2019
  • Adopted:
  • Online: July 05,2019
  • Published: July 15,2019
Article QR Code
You are the firstVisitors
Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-3
Address:4# South Fourth Street, Zhongguancun,Haidian, Beijing,Postal Code:100190
Phone:010-62661041 Fax: Email:csa (a) iscas.ac.cn
Technical Support:Beijing Qinyun Technology Development Co., Ltd.

Beijing Public Network Security No. 11040202500063