Computer Science ›› 2020, Vol. 47 ›› Issue (11A): 573-578.doi: 10.11896/jsjkx.191200141

• Interdiscipline & Application • Previous Articles     Next Articles

Construction of Mathematics Course Knowledge Graph and Its Reasoning

ZHANG Chun-xia, PENG Cheng, LUO Mei-qiu, NIU Zhen-dong   

  1. School of Computer Science and Technology,Beijing Institute of Technology,Beijing 100081,China
  • Online:2020-11-15 Published:2020-11-17
  • About author:ZHANG Chun-xia,born in 1974,Ph.D,associate professor.Her main research interests include big data search and mining,and knowledge graph construction,etc.
  • Supported by:
    This work was supported by the Science and Technology Innovation Plan of Beijing University of Technology (GZ2019075102) and Education and Teaching Reform Project of Beijing University of Technology(068).

Abstract: The construction of course knowledge graph has become an important research content in the fields of knowledge graph,E-learning and knowledge service and so on.This paper takes mathematics courses as the research object,constructsmathe-matics course ontology (MCO),designs a method of building mathematics course knowledge graph (MCKG) in terms of mathematics course ontology,and proposes an approach of knowledge reasoning founded on MCKG.The characteristics of MCO are that it includes mathematics course top-level ontology,mathematics course content ontology,and mathematics course exercise ontology.Mathematics course top-level ontology is to depict shared conceptualizing knowledge of different mathematics courses.Mathematics course content ontology is to describe knowledge of specific courses,while mathematics course exercise ontology is to depict intensions and properties of exercises of mathematics courses.The traits of MCKG are that hierarchical fusion of basic model and extended model,introduction of positive instances and negative instances of concepts,and organic integration with mathematics course content ontology.The characteristic of knowledge inference based on MCKG is that the taxonomy of infe-rence types is built.This taxonomy gives types of inference knowledge,and location and associated relationships in MCKG from the point view of ontology.The experiments about the discrete mathematics course show the validity of the proposed knowledge graph construction and reasoning methods.The mathematics course knowledge graph and its reasoning provide a formal explicit model of course knowledge representation,organization,and reasoning for users,and can improve knowledge service effects.

Key words: Knowledge graph of mathematics course, Knowledge reasoning, Mathematics course top-level ontology, Mathematics course content ontology

CLC Number: 

  • TP391
[1] Knowledge Graph [EB/OL].https://en.wikipedia.org/wiki/Knowledge_Graph.
[2] 中文知识图谱[EB/OL].https://baike.baidu.com/item/%E4%B8%AD%E6%96%87%E7%9F%A5%E8%AF%86%E5%9B%BE%E8%B0%B1.
[3] 2018知识图谱发展报告[EB/OL].https://www.useit.com.cn/thread-20216-1-1.html.
[4] WU X.From Big Data to Big Knowledge:HACE+BigKE[J].Journal of Computer Science,2016,43(7):3-6.
[5] XIONG F,GAO J.Entity Alignment for Cross-Lingual Know-ledge Graph with Graph Convolutional Networks[C]//The 28th International Joint Conference on Artificial Intelligence.2019:6480-6481.
[6] WANG X,WANG D,XU C,et al.Explainable Reasoning over Knowledge Graphs for Recommendation[C]//The AAAI Conference on Artificial Intelligence.2019:5329-5336.
[7] LI T,WANG C,LI H.Development and Construction of Know-ledge Graph [J].Journal of Nanjing University of Science and Technology,2017,41(1):22-34.
[8] ZHU M,PAO B,XU C.Research Progress on Development and Construction of Knowledge Graph[J].Journal of Nanjing University of Information Science and Technology(Natural Science Edition),2017,9(6):575-582.
[9] LIU Q,LI Y,DUAN H,et al.Knowledge Graph ConstructionTechniques [J].Journal of Computer Research and Development,2016,53(3):582-600.
[10] GUAN S,JIN X,JIA Y,et al.Knowledge Reasoning OverKnowledge Graph:A Survey[J].Journal of Software,2018,29(10):2966-2994.
[11] ZHANG M.Research on Construction of Course KnowledgeGraph and Search Technology [D].Wuhan:Wuhan University,2016.
[12] LIU Z,LI Z.Research on Information Theory Teaching Reform Based on Knowledge Graph Theory[J].Computer Knowledge and Technology,2018,14(12):125-127.
[13] XIE Z,LIU Y.Research on Teaching Reform of Digital Media Knowledge by Means of Knowledge Graph Modeling[J].Software Guild,2017,16(11):230-232.
[14] WANG L.Reconstruction of MOOC Courses based on Multimodal Knowledge Map from the Perspective of Deep Learning [J].Modern Education Technology,2018,28(10):100-106.
[15] ZHONG Y.Ontology-based Curriculum Knowledge Point Mo-deling of Major of Information Management and Information System [J].Information Research,2013(8):94-98.
[16] ZENG Q,CAO C,SUI Y,et al.Research on Ontology-basedMathematical Knowledge Acquisition and Knowledge Heritance Mechanism [J].Microelectronics & Computer,2003,20(9):19-27.
[17] HE Z,ZHUANG Y.Discrete Mathematics Course Autonomous Learning System Based on Concept Map [J].Higher Education of Sciences,2018(1):90-95.
[18] LI H,YANG G.Course Development of e-Learning based on Ontology [J].Computer Engineering and Design,2010,31(4):881-884.
[19] JIANG Y.Construction and Application of Ontology-basedMathematics Knowledge Base [D].Chengdu:University of Electronic Science and Technology of China,2011.
[20] LV J,YU X.Ontology Modeling and Reasoning for Curriculum Knowledge[J].Computer Engineering,2011,37(4):61-63.
[21] GRUBER T R.Toward Principles for the Design of Ontologies Used for Knowledge Sharing?[J].International Journal of Human-Computer Studies,1995,43(5/6):907-928.
[22] MIAO Z.Research on Technologies for Building Ontology Semi-Automatically [J].Journal of PLA University of Science and Technology,2006,7(5):426-431.
[23] QU W,GENG S,ZHANG L.Discrete Mathematics [M].Beijing:Beijing Higher Education Press,2017.
[24] Apache Jena[EB/OL].http://jena.apache.org/ .[2018].
[25] 数学课程特性[EB/OL].https://baike.baidu.com/item/%E6%95%B0%E5%AD%A6%E8%AF%BE%E7%A8%8B%E7%89%B9%E6%80%A7/19145562.
[26] 浅谈数学的特点[EB/OL].https://www.xzbu.com/9/view-4180391.htm.
[1] LI Zhi-xing, REN Shi-ya, WANG Hua-ming, SHEN Ke. Knowledge Reasoning Method Based on Unstructured Text-enhanced Association Rules [J]. Computer Science, 2019, 46(11): 209-215.
[2] XU Feng-sheng, YU Xiu-qing and SHI Kai-quan. Intelligent Discovery of Dynamic Knowledges and Logic Dynamic Relation between their Attributes [J]. Computer Science, 2015, 42(4): 160-165.
[3] ZOU Li,TAN Xue-wei and ZHANG Yun-xia. Knowledge Reasoning Based on Linguistic Truth-valued Intuitionstic Fuzzy Logic [J]. Computer Science, 2014, 41(1): 134-137.
[4] . Research on Knowledge Reasoning Technology in Emergency Command System [J]. Computer Science, 2013, 40(2): 261-264.
[5] NI Jun,CHEN Xiao-su,LIU Hui-yu,LI Jing. Research on Network Security Policy Refinement Consistency of Detection and Conflict Resolution Mechanisms [J]. Computer Science, 2011, 38(2): 32-37.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] YANG Yu-qi, ZHANG Guo-an and JIN Xi-long. Dual-cluster-head Routing Protocol Based on Vehicle Density in VANETs[J]. Computer Science, 2018, 45(4): 126 -130 .
[2] SHI Chao, XIE Zai-peng, LIU Han and LV Xin. Optimization of Container Deployment Strategy Based on Stable Matching[J]. Computer Science, 2018, 45(4): 131 -136 .
[3] LUO Xiao-yang, HUO Hong-tao, WANG Meng-si and CHEN Ya-fei. Passive Image-splicing Detection Based on Multi-residual Markov Model[J]. Computer Science, 2018, 45(4): 173 -177 .
[4] JIA Wei, HUA Qing-yi, ZHANG Min-jun, CHEN Rui, JI Xiang and WANG Bo. Mobile Interface Pattern Clustering Algorithm Based on Improved Particle Swarm Optimization[J]. Computer Science, 2018, 45(4): 220 -226 .
[5] DING Shu-yang, LI Bing and SHI Hong-bo. Study on Flexible Job-shop Scheduling Problem Based on Improved Discrete Particle Swarm Optimization Algorithm[J]. Computer Science, 2018, 45(4): 233 -239 .
[6] LI Hao-yang and FU Yun-qing. Collaborative Filtering Recommendation Algorithm Based on Tag Clustering and Item Topic[J]. Computer Science, 2018, 45(4): 247 -251 .
[7] QIN Ke-yun and LIN Hong. Relationships among Several Attribute Reduction Methods of Decision Formal Context[J]. Computer Science, 2018, 45(4): 257 -259 .
[8] XU Zhou-bo, ZHANG Kun, NING Li-hua and GU Tian-long. Summary of Graph Edit Distance[J]. Computer Science, 2018, 45(4): 11 -18 .
[9] ZHU Jin-bin, WU Ji-gang and SUI Xiu-feng. Edge Cloud Clustering Algorithm Based on Maximal Clique[J]. Computer Science, 2018, 45(4): 60 -65 .
[10] LI Hui, ZHOU Lin and XIN Wen-bo. Optimization of Networked Air-defense Operational Formation Structure Based on Bilevel Programming[J]. Computer Science, 2018, 45(4): 266 -272 .