先决条件关系信息增强的课程知识图谱关系预测方法  

Prerequisite Relation Information Enhanced Relation Prediction Method for Course Knowledge Graph

作　　者：杨佳琦 贺超波[1] 官全龙[2] 林晓凡[3] 梁卓明 罗辉琼[4] YANG Jiaqi;HE Chaobo;GUAN Quanlong;LIN Xiaofan;LIANG Zhuoming;LUO Huiqiong(School of Computer Science,South China Normal University,Guangzhou 510631,China;School of Information Science and Technology,Jinan University,Guangzhou 510632,China;School of Educational Information Technology,South China Normal University,Guangzhou 510631,China;Network Information Center,South China Normal University,Guangzhou 510631,China)

机构地区：[1]华南师范大学计算机学院,广州510631 [2]暨南大学信息科学技术学院,广州510632 [3]华南师范大学教育信息技术学院,广州510631 [4]华南师范大学网络信息中心,广州510631

出　　处：《计算机科学》2024年第10期162-169,共8页Computer Science

基　　金：国家自然科学基金(62077045);广东省基础与应用基础研究基金(2024A1515011758)。

摘　　要：大量课程知识图谱在自动答疑、学习路径规划及学习资源推荐等智能化教学应用中发挥着重要的支撑作用,然而实体间关系缺失导致的不完整问题显著降低了它们的应用价值。关系预测是自动化补全课程知识图谱缺失关系的主要手段,但现有方法仅直接使用稀疏的拓扑结构信息,未能挖掘利用其特有的先决条件关系信息进一步提升预测性能。针对该问题,设计了一种先决条件关系信息增强的课程知识图谱关系预测方法PRIERP。该方法首先设计基于语义路径计算的先决条件关系信息提取机制,然后分别基于拓扑结构信息和先决条件关系信息构建双视图,并设计有向图Transformer从双视图学习课程知识图谱的低维表征,最后基于多层感知机分类模型实现端到端的关系预测。在两个典型课程知识图谱HhsMath和ML上进行相关实验,结果表明PRIERP优于其他代表性方法。在HhsMath中,PRIERP在MRR,Hits@1,Hits@3和Hits@10评价指标上相比基线方法至少分别提升2.43%,5.93%,4.73%和1.72%。此外,关系预测的典型案例分析结果也证明了PRIERP的有效性。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.

关 键 词：课程知识图谱 关系预测 先决条件关系 图Transformer 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]