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

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

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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 reason-ing 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 gen-eral 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.

intelligent manufacturingknowledge graphaviation assemblyknowledge representationknowledge-based question an-swering

LIU PeiFeng、QIAN Lu、LU Hu、XUE Lei、ZHAO XingWei、TAO Bo

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State Key Laboratory of Digital Manufacturing Equipment and Technology,Department of Mechanical,Huazhong University of Science and Technology,Wuhan 430074,China

School of Transportation and Logistics Engineering,Wuhan University of Technology(WHUT),Wuhan 430063,China

Shanghai Aircraft Manufacturing Co.,Ltd.,Shanghai 201324,China

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of China

522750206229351491948301

2024

10.1007/s11431-023-2506-4

中国科学:技术科学(英文版)
中国科学院

中国科学:技术科学(英文版)

CSTPCDEI
影响因子：1.056
ISSN：1674-7321
年,卷(期)：2024.67(1)
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