Aiming at the huge and discrete storage of quality problem data in aerospace model quality management, a method for improving the efficiency of finding aerospace quality information by constructing a knowledge graph of aerospace quality and applying a question-answering system is proposed. The graph construction adopts a top-down construction method. First, the concept-attribute-relationship ontology model of aerospace quality knowledge is constructed. Then, based on combining expert knowledge to design the ontology concept of aerospace quality knowledge, the data to be processed is pre-processed by BERT. The training model is combined with deep learning technologies such as BiLSTM-CRF and self-attention mechanism to realize independent knowledge extraction, and then the Dice coefficient combined with the weighted average method is used to fuse the entity-relationship extraction results. Finally, based on the constructed knowledge graph, to improve the accuracy of semantic template matching, a natural language question semantic classification process model based on a weighted naive Bayesian classifier is established, which can further realize intelligent question answering. The results of the paper show that the intelligent application of knowledge graphs in the field of aerospace quality has a good prospect.


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    Titel :

    Construction and application of aerospace quality knowledge graph


    Beteiligte:
    Nayyar, Anand (Herausgeber:in) / Kolivand, Hoshang (Herausgeber:in) / Zhang, Chenyue (Autor:in) / Dong, Fangxu (Autor:in) / Long, Qinghua (Autor:in)

    Kongress:

    Fourth International Conference on Signal Processing and Computer Science (SPCS 2023) ; 2023 ; Guilin, China


    Erschienen in:

    Proc. SPIE ; 12970


    Erscheinungsdatum :

    21.12.2023





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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