用知识图谱帮中学语文老师智能选文，提升教学多样性。
T-TExTS (Teaching Text Expansion for Teacher Scaffolding): Enhancing Text Selection in High School Literature through Knowledge Graph-Based Recommendation
- 基于领域本体构建知识图谱，融合教学价值推荐文本
- Node2Vec在不同数据规模下均达0.96以上AUC，表现最优
- 结合结构与教学信号的混合模型兼顾可解释性与推荐效果
中学英语文学教师常因备课时间有限和教学资源不足，难以组建主题一致且多元的文本集合。为此，我们提出T-TExTS（Teaching Text Expansion for Teacher Scaffolding），一个基于知识图谱（KG）的推荐系统，依据教学价值而非表面元数据推荐文学文本。通过知识获取与表示方法（KNARM）构建领域专用本体，将其形式化为包含术语盒（TBox）与断言盒（ABox）的知识图谱，并在三种数据集配置（98、196、351篇文本）及两种关系加权方案下评估四种图嵌入策略（DeepWalk、有偏随机游走、混合嵌入、Node2Vec）。实验结果表明，仅靠遍历级专家加权无法超越算法结构调优：Node2Vec在所有数据规模下均取得最高AUC（0.9642–0.9750），并在大规模下各项排序指标（Hits@K、MRR、nDCG）领先。然而，通过嵌入拼接融合结构与教学信号的混合模型，在各规模下保持较高AUC（0.9122–0.9350），各项指标与Node2Vec相差数个百分点。研究证实，本体驱动的知识图谱嵌入对教育推荐系统具有重要价值，T-TExTS能有效减轻中学教师文本选择负担，支持更明智、包容的课程决策。代码开源地址：https://github.com/koncordantlab/TTExTS。
原文摘要 · Abstract (English)
High school English Literature teachers often encounter barriers to assembling diverse, thematically aligned text sets due to limited planning time and pedagogical resources. To address this need, we present T-TExTS (Teaching Text Expansion for Teacher Scaffolding), a knowledge graph (KG)-based recommendation system that suggests literature texts based on pedagogical merit rather than surface-level metadata. We construct a domain-specific ontology using the Knowledge Acquisition and Representation Methodology (KNARM), instantiate it as a knowledge graph with separate Terminological Box (TBox) and Assertional Box (ABox) components, and evaluate four graph embedding strategies (DeepWalk, biased random walk, hybrid embedding, and Node2Vec) across three dataset configurations (98, 196, and 351 texts) and two relation-weighting schemes. The experimental results reveal that traversal-level expert weighting alone does not outperform algorithmic structural tuning: Node2Vec achieves the highest Area Under the Curve (AUC) at every dataset size (0.9642--0.9750) and the strongest ranking metrics (Hits@K, MRR, nDCG) at larger scales. Combining structural and pedagogical signals through embedding concatenation, however, preserves both interpretability and competitive ranking quality, with the hybrid model maintaining a high AUC across all scales (0.9122--0.9350) and remaining within a few percentage points of Node2Vec on every ranking metric. These findings highlight the value of ontology-driven knowledge graph embeddings for educational recommendation systems and demonstrate that T-TExTS can meaningfully ease the burden of English Literature text selection for secondary educators, supporting more informed and inclusive curricular decisions. The source code for T-TExTS is available at https://github.com/koncordantlab/TTExTS.
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