Mapping Relationship Discovery of Multidimensional Architectures in Autonomous Transportation System Based on Text-Matching Model
Mapping relationships of multidimensional architectures play an essential role in the autonomous transportation system (ATS), which can help understand the complex relationships between multidimensional architectures. The current mapping relationship discovery for multidimensional architectures in A...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2023/8707205 |
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| _version_ | 1849738536364802048 |
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| author | You Liu Haonan Tuo Mingfan He Qiang Fu Tianjian Yu Jinjun Tang |
| author_facet | You Liu Haonan Tuo Mingfan He Qiang Fu Tianjian Yu Jinjun Tang |
| author_sort | You Liu |
| collection | DOAJ |
| description | Mapping relationships of multidimensional architectures play an essential role in the autonomous transportation system (ATS), which can help understand the complex relationships between multidimensional architectures. The current mapping relationship discovery for multidimensional architectures in ATS requires significant manual involvement, leading to the underutilization of textual data and the intense subjectivity of results. In order to address the above issues, it is necessary to mine and further utilize the semantic information in the textual data. This study applies the text-matching model to the mapping relationship discovery of multi-dimensional architectures, which can calculate the semantic similarity between texts. On this basis, a method based on the Siamese-BERT-wwm-Bi-LSTM model is proposed, which incorporates Chinese BERT with whole word masking (BERT-wwm), bidirectional long-short term memory (Bi-LSTM), and the Siamese Network. A series of experiments are conducted with different text-matching models. The results show that the precision rate, recall rate, and F1-score exceed 80% for most applied methods, which verifies the feasibility of using the text-matching model for mapping relationship discovery. These results expect to provide a method with good performance that can automatically perform mapping relationship discovery. |
| format | Article |
| id | doaj-art-f9f8aee4db2f4be49a33642f4f55de75 |
| institution | DOAJ |
| issn | 2042-3195 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-f9f8aee4db2f4be49a33642f4f55de752025-08-20T03:06:31ZengWileyJournal of Advanced Transportation2042-31952023-01-01202310.1155/2023/8707205Mapping Relationship Discovery of Multidimensional Architectures in Autonomous Transportation System Based on Text-Matching ModelYou Liu0Haonan Tuo1Mingfan He2Qiang Fu3Tianjian Yu4Jinjun Tang5Smart Transport Key Laboratory of Hunan ProvinceSmart Transport Key Laboratory of Hunan ProvinceSmart Transport Key Laboratory of Hunan ProvinceSmart Transport Key Laboratory of Hunan ProvinceSmart Transport Key Laboratory of Hunan ProvinceSmart Transport Key Laboratory of Hunan ProvinceMapping relationships of multidimensional architectures play an essential role in the autonomous transportation system (ATS), which can help understand the complex relationships between multidimensional architectures. The current mapping relationship discovery for multidimensional architectures in ATS requires significant manual involvement, leading to the underutilization of textual data and the intense subjectivity of results. In order to address the above issues, it is necessary to mine and further utilize the semantic information in the textual data. This study applies the text-matching model to the mapping relationship discovery of multi-dimensional architectures, which can calculate the semantic similarity between texts. On this basis, a method based on the Siamese-BERT-wwm-Bi-LSTM model is proposed, which incorporates Chinese BERT with whole word masking (BERT-wwm), bidirectional long-short term memory (Bi-LSTM), and the Siamese Network. A series of experiments are conducted with different text-matching models. The results show that the precision rate, recall rate, and F1-score exceed 80% for most applied methods, which verifies the feasibility of using the text-matching model for mapping relationship discovery. These results expect to provide a method with good performance that can automatically perform mapping relationship discovery.http://dx.doi.org/10.1155/2023/8707205 |
| spellingShingle | You Liu Haonan Tuo Mingfan He Qiang Fu Tianjian Yu Jinjun Tang Mapping Relationship Discovery of Multidimensional Architectures in Autonomous Transportation System Based on Text-Matching Model Journal of Advanced Transportation |
| title | Mapping Relationship Discovery of Multidimensional Architectures in Autonomous Transportation System Based on Text-Matching Model |
| title_full | Mapping Relationship Discovery of Multidimensional Architectures in Autonomous Transportation System Based on Text-Matching Model |
| title_fullStr | Mapping Relationship Discovery of Multidimensional Architectures in Autonomous Transportation System Based on Text-Matching Model |
| title_full_unstemmed | Mapping Relationship Discovery of Multidimensional Architectures in Autonomous Transportation System Based on Text-Matching Model |
| title_short | Mapping Relationship Discovery of Multidimensional Architectures in Autonomous Transportation System Based on Text-Matching Model |
| title_sort | mapping relationship discovery of multidimensional architectures in autonomous transportation system based on text matching model |
| url | http://dx.doi.org/10.1155/2023/8707205 |
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