国家电网数据模型知识图谱的建立及补全技术

Establishment and Completion Techniques of Knowledge Graph for National Grid Data Modeling

本文旨在通过构建知识图谱和补全图谱信息的方法,提升国家电网数据模型的可阅读性和完整性。首先,针对SGCIM数据量庞大、分类繁多的特点,构建一个全面抽象的知识图谱。其次,为了解决知识图谱中存在内容缺失的问题,引入多层卷积神经网络模型以实现对知识图谱的补全。实验的结果表明,基于电网模型数据集进行知识图谱补全实验的评价指标相较于基准模型R-GCN平均提升了12%,模型效果得到了验证。

This study aims to improve the readability and completeness of the State Grid data model by constructing a knowledge graph is constructed for the characteristics of SG-CIM,which is characterized by a huge amount of data and many classifications.Second,in order to solve the problem of missing content in the knowledge graph,a multi-layer convolutional neural network model is introduced to realize the complementation of the knowledge graph.The results of the experiments show that the evaluation indexes of the knowledge graph complementation experiments based on the grid model dataset are improved by 12%on average compared with the benchmark model R-GCN,and the model effect is verified.