GPB and BAC: two novel models towards building an intelligent motor fault maintenance question answering system

Generally, the existing methods for constructing a knowledge graph used in a question answering system adopted two different models respectively, one is for identifying entities, and the other is for extracting relationships between entities. However, this method may reduce the quality of knowledge because it is very difficult to keep contextual information consistent with the same entities in the two different models. To address this issue, this paper proposes a model called GPB (GlobalPointer + BiLSTM) which integrates the BiLSTM into GlobalPointer through concatenation operations to simultaneously guarantee the rationality of identified entities and relationships between entities. In addition, to enhance the user experience using an intelligent motor fault maintenance question answering system, a model called BAC (BiLSTM + Attention + CRF) is proposed to identify named entities in user questions, and the BERT-wwm model is used to classify user intentions to improve the quality of answers. Finally, to verify the advantages of the proposed model GPB and BAC, comparative experiments and real application effects of the developed question answering system are demonstrated on our built motor fault maintenance dataset. The experimental results indicate that the constructed knowledge graph and developed question answering system provide engineers with high-quality motor maintenance knowledge services.