In this paper, we propose a method for knowledge graph construction in power distribution networks. This method leverages entity features, which involve their semantic, phonetic, and syntactic characteristics, in both the knowledge graph of distribution network and the dispatching texts. An enhanced model based on Convolutional Neural Network, is utilized for effectively matching dispatch text entities with those in the knowledge graph. The effectiveness of this model is evaluated through experiments in real-world power distribution dispatch scenarios. The results indicate that, compared with the baselines, the proposed model excels in linking a variety of entity types, demonstrating high overall accuracy in power distribution knowledge graph construction task.

翻译：本文提出了一种配电网知识图谱构建方法。该方法利用配电网知识图谱与调度文本中实体的语义、语音及句法特征，基于卷积神经网络（CNN）的改进模型实现调度文本实体与知识图谱实体的有效匹配。通过实际配电网调度场景的试验验证，结果表明：与基线模型相比，所提模型在多种实体类型的链接任务中表现优异，在配电网知识图谱构建任务中展现出较高的整体准确性。