Knowledge graph construction of component quality management

Abstract

With the development of Industrial Internet of Things, the types and functions of components are increasing, the application environment is becoming more and more complex. Also, the quality management of components is becoming more and more important. In order to understand the knowledge related to component quality management more conveniently and build an intelligent system for component quality management, this paper proposes a method to construct component quality management knowledge graph based on BERT word embedding model and entity relationship joint extraction method based on annotation strategy. Combining entity extraction and relationship extraction parts into one not only reduces the consumption of computing resources, but also reduces the propagation of wrong entities. In this paper, the sequence to sequence model of Bert-BilSTm-CRF is adopted. Through the BERT word embedding layer, the context information can be better utilized and the accuracy of extraction can be improved. Experimental results show that compared with other classical deep learning term extraction models, this model has a significant improvement in accuracy, recall rate and F1 value.