大数据2026，Vol.12Issue(4)：117-136,20.DOI:10.11959/j.issn.2096-0271.2025077
基于拓扑结构与相似度信息融合的教育知识图谱节点重要性评估模型
Node importance evaluation model for educational knowledge graph based on topological structure and similarity information fusion
摘要
Abstract
Educational knowledge graph is an important tool for representing relationships between knowledge and concept in the field of education.Understanding and evaluating the importance of node in educational knowledge graph are crucial for tasks like educational resource management and learning path recommendation.However,traditional node importance evaluation methods often treat all nodes equally,considering only certain topological structures,and fail to capture the comprehensive characteristics of node.To address this,we proposed TSFM driven by key node.TSFM evaluated node importance by integrating topological structure with semantic and structural similarity information driven by key node.Specifically,TSFM utilized graph neural networks to embed nodes in knowledge graph,and optimized the embedding representation by considering topological and semantic similarity between nodes.Experimental results demonstrate that TSFM outperforms traditional algorithms and recent graph neural network models across multiple evaluation metrics.关键词
教育知识图谱/节点重要性评估/拓扑结构/图注意力网络Key words
educational knowledge graph/node importance evaluation/topological structure/graph attention network分类
信息技术与安全科学引用本文复制引用
李美子,伍云芳,卢淑怡,王浩,杨茹..基于拓扑结构与相似度信息融合的教育知识图谱节点重要性评估模型[J].大数据,2026,12(4):117-136,20.基金项目
国家自然科学基金项目(No.62477032) （No.62477032）
国家自然科学基金项目(No.62302306) （No.62302306）
国家自然科学基金项目(No.62372300) The National Natural Science Foundation of China(No.62477032),The National Natural Science Foundation of China(No.62302306),The National Natural Science Foundation of China(No.62372300) （No.62372300）
