用知识图谱帮中学英语老师选更合适的教学文献。
LIT-GRAPH: Evaluating Deep vs. Shallow Graph Embeddings for High-Quality Text Recommendation in Domain-Specific Knowledge Graphs
- 对比四种图嵌入方法，发现深度模型更懂教学语义。
- R-GCN在语义排序上显著优于浅层模型，推荐更精准。
- 适合教育领域研究者和智能教学系统开发者。
本研究提出LIT-GRAPH（文学推荐与教学启发知识图谱），一个基于知识图谱的推荐系统，旨在帮助中学英语教师选择多样且符合教学目标的教材。系统基于英语文学本体，解决课程内容僵化问题，比较了四种图嵌入方法：DeepWalk、偏置随机游走（BRW）、混合模型（连接DeepWalk与BRW向量）以及深度模型关系图卷积网络（R-GCN）。结果表明：虽然浅层模型在结构链接预测上表现更好，但R-GCN在语义排序任务中全面领先。通过关系特定的消息传递机制，深度模型更关注教学相关性而非简单连通性，从而生成高质量、领域特定的推荐结果。
原文摘要 · Abstract (English)
This study presents LIT-GRAPH (Literature Graph for Recommendation and Pedagogical Heuristics), a novel knowledge graph-based recommendation system designed to scaffold high school English teachers in selecting diverse, pedagogically aligned instructional literature. The system is built upon an ontology for English literature, addressing the challenge of curriculum stagnation, where we compare four graph embedding paradigms: DeepWalk, Biased Random Walk (BRW), Hybrid (concatenated DeepWalk and BRW vectors), and the deep model Relational Graph Convolutional Network (R-GCN). Results reveal a critical divergence: while shallow models excelled in structural link prediction, R-GCN dominated semantic ranking. By leveraging relation-specific message passing, the deep model prioritizes pedagogical relevance over raw connectivity, resulting in superior, high-quality, domain-specific recommendations.
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