Hypergraph embedding-based representation method for multi-nary relational knowledge of bridge crane faults

被引：0
作者：
Zhang F. [1 ]
Zhou B. [1 ]
Bao J. [1 ]
Li X. [1 ]
机构：
[1] College of Mechanical Engineering, Donghua University, Shanghai
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2024年 / 30卷 / 02期
关键词：
bridge crane fault; graph embedding algorithm; knowledge hypergraph; knowledge representation; multi-nary relation;
D O I：
10.13196/j.cims.2022.0916
中图分类号：
学科分类号：
摘要：
The conventional knowledge graph can only deal with binary relations, while knowledge of bridge crane faults contains a large number of multi-nary relations of "multiple phenomena, multiple causes, and multiple methods". If forced to transform, the integrity of the relations will be destroyed, causing serious information distortion. To deal with such complex multi-nary relational knowledge to ensure integrity, the Knowledge hypergraph was proposed and a hypergraph embedding-based representation method for multi-nary relational knowledge of bridge crane faults was designed. Through sorting the correlation among phenomena, causes, methods and other data in the driving fault sheet, a driving fault ontology model suitable for characterizing the multi-nary relation was constructed, which was taken as the schema for establishing the knowledge hypergraph of bridge crane faults. Based on the BERT model in natural language processing and the hypergraph convolutional network, the embedding representation of fault knowledge was obtained, hence similar fault retrieval could be carried out. By exploiting the fault sheets of bridge cranes collected from a steel factory, the effectiveness of the proposed method was verified. © 2024 CIMS. All rights reserved.
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页码：445 / 459
页数：14
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