The joint knowledge reasoning model based on knowledge representation learning for aviation assembly domain

Knowledge graph technology is widely applied in the domain of general knowledge reasoning with an excellent performance. For fine-grained professional fields, professional knowledge graphs can provide more accurate information in practical industrial scenarios. Based on an aviation assembly domain-specific knowledge graph, the article constructs a joint knowledge reasoning model, which combines a named entity recognition model and a subgraph embedding learning model. When performing knowledge reasoning tasks, the two models vectorize entities, relationships and entity attributes in the same space, so as to share parameters and optimize learning efficiency. The knowledge reasoning model, which provides intelligent question answering services, is able to reduce the assembly error rate and improve the assembly efficiency. The system can accurately solve general knowledge reasoning problems in the assembly process in actual industrial scenarios of general assembly and component assembly under interference-free conditions. Finally, this paper compares the proposed knowledge reasoning model based on knowledge representation learning and the question-answering system based on large-scale pre-trained models. In the application scenario of system functional testing in general assembly, the joint model attains an accuracy rate of 95%, outperforming GPT with 78% accuracy and enhanced representation through knowledge integration with 71% accuracy.