Assembly Information Model Based on Knowledge Graph

被引:13
作者
Chen Z. [1 ]
Bao J. [1 ]
Zheng X. [1 ]
Liu T. [1 ]
机构
[1] College of Mechanical Engineering, Donghua University, Shanghai
基金
中国国家自然科学基金;
关键词
A; assembly process; information model; integrating; knowledge graph; TP; 391;
D O I
10.1007/s12204-020-2179-y
中图分类号
学科分类号
摘要
There are heterogeneous problems between the CAD model and the assembly process document. In the planning stage of assembly process, these heterogeneous problems can decrease the efficiency of information interaction. Based on knowledge graph, this paper proposes an assembly information model (KGAM) to integrate geometric information from CAD model, non-geometric information and semantic information from assembly process document. KGAM describes the integrated assembly process information as a knowledge graph in the form of “entity-relationship-entity” and “entity-attribute-value”, which can improve the efficiency of information interaction. Taking the trial assembly stage of a certain type of aero-engine compressor rotor component as an example, KGAM is used to get its assembly process knowledge graph. The trial data show the query and update rate of assembly attribute information is improved by more than once. And the query and update rate of assembly semantic information is improved by more than twice. In conclusion, KGAM can solve the heterogeneous problems between the CAD model and the assembly process document and improve the information interaction efficiency. © 2020, Shanghai Jiao Tong University and Springer-Verlag GmbH Germany, part of Springer Nature.
引用
收藏
页码:578 / 588
页数:10
相关论文
共 50 条
[31]   The concept information of graph granule with application to knowledge graph embedding [J].
Niu, Jiaojiao ;
Chen, Degang ;
Ma, Yinglong ;
Li, Jinhai .
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2024, 15 (12) :5595-5606
[32]   Interpretable Emotion Analysis Based on Knowledge Graph and OCC Model [J].
Wang, Shuo ;
Zhang, Yifei ;
Lin, Bochen ;
Li, Boxun .
PROCEEDINGS OF THE 31ST ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, CIKM 2022, 2022, :2038-2045
[33]   Research on knowledge graph alignment model based on deep learning [J].
Yu, Chuanming ;
Wang, Feng ;
Liu, Ying-Hsang ;
An, Lu .
EXPERT SYSTEMS WITH APPLICATIONS, 2021, 186
[34]   Knowledge Graph Construction Based on a Joint Model for Equipment Maintenance [J].
Lou, Ping ;
Yu, Dan ;
Jiang, Xuemei ;
Hu, Jiwei ;
Zeng, Yuhang ;
Fan, Chuannian .
MATHEMATICS, 2023, 11 (17)
[35]   Knowledge Graph Recommendation Model Based on Feature Space Fusion [J].
Zhang, Suqi ;
Wang, Xinxin ;
Wang, Rui ;
Gu, Junhua ;
Li, Jianxin .
APPLIED SCIENCES-BASEL, 2022, 12 (17)
[36]   A Temporal Knowledge Graph Embedding Model Based on Variable Translation [J].
Han, Yadan ;
Lu, Guangquan ;
Zhang, Shichao ;
Zhang, Liang ;
Zou, Cuifang ;
Wen, Guoqiu .
TSINGHUA SCIENCE AND TECHNOLOGY, 2024, 29 (05) :1554-1565
[37]   A Novel Embedding Model for Knowledge Graph Completion Based on Quaternion [J].
Gao, Haipeng ;
Yang, Kun ;
Yang, Yuxue ;
Qin, Ke .
2021 IEEE 9TH INTERNATIONAL CONFERENCE ON INFORMATION, COMMUNICATION AND NETWORKS (ICICN 2021), 2021, :470-474
[38]   Knowledge Graph Completion Model Based on Entity and Relation Fusion [J].
Zhengang Z. ;
Chuanming Y. .
Data Analysis and Knowledge Discovery, 2023, 7 (02) :15-25
[39]   Topic Model Based Knowledge Graph for Entity Similarity Measuring [J].
Sun, Haoran ;
Ren, Rui ;
Cai, Hongming ;
Xu, Boyi ;
Liu, Yonggang ;
Li, Tongyu .
2018 IEEE 15TH INTERNATIONAL CONFERENCE ON E-BUSINESS ENGINEERING (ICEBE 2018), 2018, :94-101
[40]   A Personalized Attractions Recommendation Model based on Tourism Knowledge graph [J].
Jiang, Qi .
INTERNATIONAL CONFERENCE ON ENVIRONMENTAL REMOTE SENSING AND BIG DATA (ERSBD 2021), 2021, 12129