Building a Large-Scale Knowledge Graph for Elementary Education in China

With the penetration of information technology into all areas of society, Internet-assisted education has become an important opportunity for current educational reform. In order to better assist in teaching and learning, help students deepen their understanding and absorption of knowledge. We build a knowledge graph for elementary education, firstly, we define elementary education ontology, divide the knowledge graph into three sub-graphs. Then extracting concept instance and relation instance form textbook and existing knowledge base based on unsupervised method. In addition, we have acquired four different learning resources to assist in learning. At last, the results show that the procedure we proposed is scientific and efficient.

[1]  Lei Shi,et al.  DKG: An Expanded Knowledge Base for Online Course , 2017, DASFAA Workshops.

[2]  Ruifang Liu,et al.  The construction of high educational knowledge graph based on MOOC , 2017, 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC).

[3]  N. J. Schweitzer Wikipedia and Psychology: Coverage of Concepts and Its Use by Undergraduate Students , 2008 .

[4]  Seung-won Hwang,et al.  Graph-Based Wrong IsA Relation Detection in a Large-Scale Lexical Taxonomy , 2017, AAAI.

[5]  Ji-Rong Wen,et al.  An Inference Approach to Basic Level of Categorization , 2015, CIKM.

[6]  Sarah J. Tracy,et al.  Wikipedia as Public Scholarship: Communicating Our Impact Online , 2010 .

[7]  Penghe Chen,et al.  KnowEdu: A System to Construct Knowledge Graph for Education , 2018, IEEE Access.

[8]  Finn Årup Nielsen,et al.  Wikipedia in the eyes of its beholders: A systematic review of scholarly research on Wikipedia readers and readership , 2014, J. Assoc. Inf. Sci. Technol..

[9]  Jens Lehmann,et al.  DBpedia: A Nucleus for a Web of Open Data , 2007, ISWC/ASWC.

[10]  Marian Bucos,et al.  Designing a semantic web ontology for E-learning in higher education , 2010, 2010 9th International Symposium on Electronics and Telecommunications.

[11]  Yoshua Bengio,et al.  Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.

[12]  Zhaohui Wu,et al.  Using Prerequisites to Extract Concept Maps fromTextbooks , 2016, CIKM.

[13]  Jeongmin Kim,et al.  An Ontological Approach for Semantic Modeling of Curriculum and Syllabus in Higher Education , 2016 .

[14]  Wenyi Huang,et al.  Measuring Prerequisite Relations Among Concepts , 2015, EMNLP.

[15]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[16]  Haixun Wang,et al.  Probase: a probabilistic taxonomy for text understanding , 2012, SIGMOD Conference.

[17]  Bin Xu,et al.  An Approach of Ontology Based Knowledge Base Construction for Chinese K12 Education , 2016, 2016 First International Conference on Multimedia and Image Processing (ICMIP).