To use educational resources efficiently and dig out the nature of relations among MOOCs(massive open online courses),a knowledge graph was built for MOOCs on four major platforms:Coursera,EDX,XuetangX,and ICourse.Thi...To use educational resources efficiently and dig out the nature of relations among MOOCs(massive open online courses),a knowledge graph was built for MOOCs on four major platforms:Coursera,EDX,XuetangX,and ICourse.This paper demonstrates the whole process of educational knowledge graph construction for reference.And this knowledge graph,the largest knowledge graph of MOOC resources at present,stores and represents five classes,11 kinds of relations and 52779 entities with their corresponding properties,amounting to more than 300000 triples.Notably,24188 concepts are extracted from text attributes of MOOCs and linked them directly with corresponding Wikipedia entries or the closest entries calculated semantically,which provides the normalized representation of knowledge and a more precise description for MOOCs far more than enriching words with explanatory links.Besides,prerequisites discovered by direct extractions are viewed as an essential supplement to augment the connectivity in the knowledge graph.This knowledge graph could be considered as a collection of unified MOOC resources for learners and the abundant data for researchers on MOOC-related applications,such as prerequisites mining.展开更多
基金supported by the National Key Research and Development Program of China under Grant No.2018YFB1004502the National Natural Science Foundation of China under Grant Nos.61532001,61702532 and 61303190.
文摘To use educational resources efficiently and dig out the nature of relations among MOOCs(massive open online courses),a knowledge graph was built for MOOCs on four major platforms:Coursera,EDX,XuetangX,and ICourse.This paper demonstrates the whole process of educational knowledge graph construction for reference.And this knowledge graph,the largest knowledge graph of MOOC resources at present,stores and represents five classes,11 kinds of relations and 52779 entities with their corresponding properties,amounting to more than 300000 triples.Notably,24188 concepts are extracted from text attributes of MOOCs and linked them directly with corresponding Wikipedia entries or the closest entries calculated semantically,which provides the normalized representation of knowledge and a more precise description for MOOCs far more than enriching words with explanatory links.Besides,prerequisites discovered by direct extractions are viewed as an essential supplement to augment the connectivity in the knowledge graph.This knowledge graph could be considered as a collection of unified MOOC resources for learners and the abundant data for researchers on MOOC-related applications,such as prerequisites mining.
