Construction and Application of Failure Knowledge Graph in Automobile Field
Knowledge graph technology is of great significance to the efficient fault diagnosis of automobiles.The existing construc-tion of the knowledge graph of automobile faults has problems such as a poor entity recognition model and the inability to solve nested entities.In order to solve the above problems,an improved nested entity recognition model was proposed by adopting the pre-training semantic model of whole word mask,adding adversarial training,and improving the nested entity recognition model.The experimental results show that the proposed model is 3.56%,4.08%,and 3.05%higher than the baseline model in terms of the F1 value(F1),accuracy(P)and recall(R),and also has different degrees of improvement compared with other models,which verifies that the pro-posed model has a significant effect on entity recognition in the field of automobile maintenance.At the same time,based on the con-structed automobile fault knowledge graph,the intelligent question answering prototype system of automobile fault knowledge is real-ized,and the application prospect of knowledge graph technology in the field of automobile fault diagnosis and maintenance is shown.
automobile maintenanceknowledge graphnested named entity recognitionpre-training language modeladversarial training
万方数据
维普
