基于GRU和PCNN的电力知识抽取

Knowledge Extraction in Electric Power Based on GRU and PCNN

构造电力系统知识图谱最重要的就是电力知识的抽取,针对目前传统基于监督学习、单一神经网络模型存在的问题和缺点,如CNN擅长提取局部最重要特征而不适合处理序列输入;RNN在处理序列化任务占优势却对于重要特征的提取很乏力,因此本文改进了一种基于GRU和PCNN的模型,该模型可以有效解决传统模型的不足,通过结合GRU模型和PCNN模型的优点,实验结果表明该方法相比传统方法效果极佳,可以有效实现对电力系统知识抽取.

The main part of drawing the knowledge map of electrical power systems is the extraction of power knowledge.In the traditional supervised-learning-based single neural network models,CNN performs well in extracting the most important local features but is not suitable for processing sequence input,and RNN is strong in tackling serialization tasks but weak in extracting important features.To solve these problems,this study puts forward a model based on GRU and PCNN.Compared with traditional models,this model combining the advantages of the GRU helped model and the PCNN model can obtain impressive results and effectively extract the knowledge of electrical power systems.