基于知识表示学习的公共计算机课程管理研究

为解决假负例和零损失问题, 将 Transformer 网络结构用于提取知识库中的实体描述信息, 用带有约束性的自注意力机制寻找最能表达实体意义的表示子空间以增强实体的表示能力, 引入对抗生成网络思想生成负样本, 提升了知识表示能力。 该方法在公共计算机课程知识图谱的构建中, 清楚描绘了课程知识点间的内在关系, 对于指导课程设置与进度安排, 引导学生学习具有十分重要的意义。

Research on Management of Public Computer Course Based on Knowledge of Representation Learning

To solve the problem of false negative examples and zero loss, the transformer network structure is used to extract the entity description information in the knowledge base. The self attention mechanism with constraints is used to find the representation subspace that can best express the entity meaning to enhance the entity representation ability. The idea of counter generated network is introduced to generate negative samples,which improves the knowledge representation ability. In the construction of the knowledge map of public computer course, this method clearly describes the internal relationship between the knowledge points of the course, which is of great significance for guiding the curriculum setting and schedule arrangement and guiding students to learn.