To address the issue of poor embedding performance in the knowledge graph of a programming design course, a joint represen-tation learning model that combines entity neighborhood infor-mation and description information is proposed. Firstly, a graph at-tention network is employed to obtain the features of entity neigh-boring nodes, incorporating relationship features to enrich the structural information. Next, the BERT-WWM model is utilized in conjunction with attention mechanisms to obtain the representation of entity description information. Finally, the final entity vector representation is obtained by combining the vector representations of entity neighborhood information and description information. Experimental results demonstrate that the proposed model achieves favorable performance on the knowledge graph dataset of the pro-gramming design course, outperforming other baseline models.

翻译：针对程序设计课程知识图谱中嵌入性能不佳的问题，提出了一种融合实体邻域信息与描述信息的联合表示学习模型。首先，利用图注意力网络获取实体邻接节点特征，并引入关系特征以丰富结构信息；其次，采用BERT-WWM模型结合注意力机制获取实体描述信息的表示；最后，通过融合实体邻域信息与描述信息的向量表示，得到最终的实体向量表示。实验结果表明，所提模型在程序设计课程知识图谱数据集上取得了良好的性能，优于其他基线模型。