Prerequisite Relation Information Enhanced Relation Prediction Method for Course KnowledgeGraph

A large amount of course knowledge graphs have played a crucial role in intelligent teaching applications such as automatic Q&A,learning path planning,and learning resource recommendation.However,the incompleteness issue caused by missing entity relations significantly reduces their application value.Relation prediction is the primary means of automatically completing the missing relations in course knowledge graphs,but existing methods only directly use sparse topology information and fail to exploit and enhance the prediction performance by further using its unique prerequisite relation information.To address this pro-blem,a course knowledge graph relation prediction method,prerequisite relation information enhanced relation prediction(PRIERP),is proposed.This method first designs a prerequisite relation information extraction mechanism based on semantic path computation.Then,it constructs dual views based on topology information and prerequisite relation information,and designs a directed graph Transformer to learn the low-dimentional representation of the course knowledge graph from the dual views.Finally,an end-to-end relation prediction is achieved based on a multi-layer perceptron classification model.Experiments are conducted on two typical course knowledge graphs HhsMath and ML.The results demonstrate that PRIERP outperforms other representative methods.In HhsMath,PRIERP achieves at least 2.43%,5.93%,4.73% and 1.72% improvements in terms of MRR,Hits@1,Hits@3,and Hits@10 metrics,respectively.Furthermore,the analysis of typical cases in relation prediction also confirms its effectiveness.