ProPRL：教育知识图谱中属性感知的前置关系学习
ProPRL: Property-Aware Prerequisite Relation Learning in Educational Knowledge Graphs
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中文总结 AI 辅助
ProPRL是一种属性感知的教育知识图谱前置关系学习框架，通过多视图融合与反对称正则化抑制反向预测，在真实教育数据集上实现了该任务的最先进性能。
中文摘要 AI 辅助
前置关系学习是自适应教学的核心，但现有方法常将其表述为常规链接预测，限制了其为每个候选对自适应整合互补教育证据的能力，也难以抑制矛盾的反向预测。我们提出ProPRL，一种属性感知的前置关系学习框架。ProPRL首先从概念-资源超图和有向学习行为图中学习互补的概念表示，其中保向个性化传播聚合多跳行为证据；接着采用对条件门为每个候选有序概念对自适应加权并融合两种视图；最后，不可逆性约束引入反对称正则化项，惩罚同一概念对两个方向同时出现高置信度的情况。在多个真实教育数据集上的实验表明，ProPRL在前置关系学习任务上达到了最先进的性能。
英文摘要
Prerequisite relation learning is central to adaptive instruction, yet existing methods often formulate it as conventional link prediction, limiting their ability to adaptively integrate complementary educational evidence for individual candidate pairs and to discourage contradictory reverse predictions. We propose ProPRL, a Property-aware Prerequisite Relation Learning framework. ProPRL first learns complementary concept representations from a concept-resource hypergraph and a directed learning-behavior graph, where direction-preserving personalized propagation aggregates multi-hop behavioral evidence. It then employs a Pair-conditioned Gate to adaptively weight and fuse the two views for each candidate ordered concept pair. Finally, an \textit{Irreversibility Constraint} introduces an anti-symmetry regularizer that penalizes simultaneously high confidence in both directions of the same concept pair. Experiments on multiple real-world educational datasets show that ProPRL achieves state-of-the-art performance on prerequisite relation learning.
