基于双图神经网络的先序关系挖掘  

Learning with Dual-graph for Concept Prerequisite Discovering

作　　者：徐国兰[1] 白如江[2] Xu Guolan;Bai Rujiang(Shandong University of Technology Library,Zibo 255000,China;School of Information Management,Shandong University of Technology,Zibo 255000,China)

机构地区：[1]山东理工大学图书馆,淄博255000 [2]山东理工大学信息管理学院,淄博255000

出　　处：《数据分析与知识发现》2024年第5期38-45,共8页Data Analysis and Knowledge Discovery

基　　金：国家社会科学基金项目(项目编号:21BTQ071)的研究成果之一。

摘　　要：【目的】充分利用概念在学习资源中的提及等细粒度信息,更有效地进行先序关系挖掘。【方法】利用双图神经网络进行先序关系挖掘。根据概念与学习资源之间的联系以及概念之间的先序关系分别建立概念语义图和概念先序图。使用图神经网络对其学习,得到概念的表示并用于未知先序关系预测。【结果】通过在4个经典先序关系挖掘数据集上进行大量的实验,本文方法取得了较好的结果,并超过了现有的方法,在F1指标上分别超过次优方法0.059、0.037、0.073、0.042。【局限】本文方法对在学习资源中有明确提及的概念有较强的先序关系挖掘能力,而对未在学习资源中出现过的概念预测能力较弱。【结论】双图神经网络方法能够充分利用学习资源中的语义信息,提升先序关系挖掘能力。[Objective]This paper fully utilizes fine-grained information,such as the mention of concepts in learning resources,to more effectively identify prerequisite relationships.[Methods]First,we explored prerequisite relationships using a dual-graph neural network.Then,we constructed a concept semantic graph and a concept prerequisite graph based on the connections between learning resources and concepts.Third,we obtained the representations of concepts with a graph neural network and predicted the unknown prerequisite relationships.[Results]We extensively examined our model on four classic prerequisite relationship mining datasets.Our method achieved promising results,surpassing existing methods.It outperformed the second-best method by 0.059,0.037,0.073,and 0.042 regarding the F1 score on each dataset.[Limitations]This method shows weak predictive ability for concepts not appearing in the learning resources.[Conclusions]The proposed dual-graph neural network method can effectively leverage semantic information in learning resources to enhance prerequisite relationship mining.

关 键 词：先序关系挖掘 图神经网络 智慧教育 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]