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