- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
- Scopus
- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
- Chinese S&T Journal Citation Reports
- Chinese Science Citation Database
| Citation: | 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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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.
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