Knowledge Fusion Method of High-Speed Train Based on Knowledge Graph

WANG Shuying; LI Xue; LI Rong; ZHANG Haizhu

doi:10.3969/j.issn.0258-2724.20220193

Volume 59 Issue 5

Oct. 2024

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Journal of Southwest Jiaotong University > 2024 > 59(5): 1194-1203.

LIU Wei, LIU Tongtong, WANG Hui, LI Kunpeng, ZHANG Jian, SANG Guoyang, WU Tuojian. Dynamic Simulation of Load Process for Urban Rail Power Supply System Driven by Operation Diagram[J]. Journal of Southwest Jiaotong University, 2022, 57(5): 967-975. doi: 10.3969/j.issn.0258-2724.20200752

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WANG Shuying, LI Xue, LI Rong, ZHANG Haizhu. Knowledge Fusion Method of High-Speed Train Based on Knowledge Graph[J]. Journal of Southwest Jiaotong University, 2024, 59(5): 1194-1203. doi: 10.3969/j.issn.0258-2724.20220193

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Knowledge Fusion Method of High-Speed Train Based on Knowledge Graph

doi: 10.3969/j.issn.0258-2724.20220193

1.
School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China
2.
School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China

Received Date: 16 Mar 2022
Rev Recd Date: 04 Jul 2022

Available Online: 29 May 2024

Publish Date: 11 Jul 2022

Abstract

Abstract

To address challenges of unclear correlation, intricate knowledge retrieval, and difficult knowledge application across diverse domains of high-speed trains, the organizational structure involving multi-source heterogeneous knowledge pertaining to high-speed trains was first analyzed, and a knowledge graph pattern layer and knowledge graph of the high-speed train domain was developed based on the product structure tree and stage domain of high-speed trains. Subsequently, the bidirectional encoder transformer-bidirectional long short-term memory network-conditional random field (BERT-BILSTM-CRF) model was employed for entity recognition, so as to establish the mapping of stage domain ontology. Then, the entity attributes of high-speed trains were categorized into structured and unstructured attributes. The Levenshtein distance and the continuous bag of words-bidirectional long short-term memory network (CBOW-BILSTM) model were utilized to calculate the similarity of corresponding attributes, resulting in aligned entity pairs. Ultimately, the knowledge fusion graph of high-speed train domain fusion was constructed by using the coding structure tree of high-speed train products for mapping and fusion. The proposed method was applied to high-speed train bogies for verification. The results reveal that in terms of named entity recognition, the entity recognition accuracy of the BERT-BILSTM-CRF model reaches 91%. In terms of entity alignment, the F1 values (the harmonic mean of accuracy and recall) of entity similarity calculated by the Levenshtein distance and the CBOW-BILSTM model are 82% and 83%, respectively.
- high-speed train,
- knowledge graph,
- knowledge fusion,
- ontology mapping,
- entity alignment

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References(18)

References

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