Graph-Enhanced Deep Learning With Character Similarity Mining for Automated NOTAM Correction in Aviation Systems
As the number of Notices to Airmen (NOTAMs) has increased exponentially, the existing manual processing methods have become increasingly inefficient and error-prone. To address this challenge, we used raw NOTAM data from the Civil Aviation Information Center, from September 2021 to September 2023. A...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11029232/ |
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| author | Bing Dong Haoran Yao Chuang Luo Ruichao Yang Ziyue Wang |
| author_facet | Bing Dong Haoran Yao Chuang Luo Ruichao Yang Ziyue Wang |
| author_sort | Bing Dong |
| collection | DOAJ |
| description | As the number of Notices to Airmen (NOTAMs) has increased exponentially, the existing manual processing methods have become increasingly inefficient and error-prone. To address this challenge, we used raw NOTAM data from the Civil Aviation Information Center, from September 2021 to September 2023. A trained relational graph convolutional neural network (CEV-RGCN) model, along with a tianzege-convolutional neural network (CNN), were employed to calculate the feature similarity of Chinese characters based on phonetics and glyphs. Based on this analysis, separate knowledge graphs for phonetics and glyphs were constructed, forming the foundation for a similar character library. During the masking stage of the CKBERT model, both positive and negative samples of knowledge graph triples were generated and integrated into multi-hop comparative learning. Candidate characters are retrieved from the similar character database, and the appropriate character is selected for replacement in the original text based on contextual information and similarity. This led to the development of the Ctc-CKBERT model, which significantly enhanced both computational efficiency and accuracy. Characters with a similarity score ranging from 0.9 to 1 were identified and applied based on the NOTAM dataset, thereby improving the accuracy of text error correction. |
| format | Article |
| id | doaj-art-c385ccfe53d44ff8aaeb17b250206f1b |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c385ccfe53d44ff8aaeb17b250206f1b2025-08-20T02:37:05ZengIEEEIEEE Access2169-35362025-01-011310226510227710.1109/ACCESS.2025.357833511029232Graph-Enhanced Deep Learning With Character Similarity Mining for Automated NOTAM Correction in Aviation SystemsBing Dong0https://orcid.org/0000-0001-5940-2803Haoran Yao1https://orcid.org/0009-0007-7396-9065Chuang Luo2Ruichao Yang3https://orcid.org/0009-0001-3125-0446Ziyue Wang4https://orcid.org/0009-0008-3883-1295Air Traffic Management Academy, Civil Aviation Flight University of China, Guanghan, Sichuan, ChinaAir Traffic Management Academy, Civil Aviation Flight University of China, Guanghan, Sichuan, ChinaAir Traffic Management Academy, Civil Aviation Flight University of China, Guanghan, Sichuan, ChinaAir Traffic Management Academy, Civil Aviation Flight University of China, Guanghan, Sichuan, ChinaAir Traffic Management Academy, Civil Aviation Flight University of China, Guanghan, Sichuan, ChinaAs the number of Notices to Airmen (NOTAMs) has increased exponentially, the existing manual processing methods have become increasingly inefficient and error-prone. To address this challenge, we used raw NOTAM data from the Civil Aviation Information Center, from September 2021 to September 2023. A trained relational graph convolutional neural network (CEV-RGCN) model, along with a tianzege-convolutional neural network (CNN), were employed to calculate the feature similarity of Chinese characters based on phonetics and glyphs. Based on this analysis, separate knowledge graphs for phonetics and glyphs were constructed, forming the foundation for a similar character library. During the masking stage of the CKBERT model, both positive and negative samples of knowledge graph triples were generated and integrated into multi-hop comparative learning. Candidate characters are retrieved from the similar character database, and the appropriate character is selected for replacement in the original text based on contextual information and similarity. This led to the development of the Ctc-CKBERT model, which significantly enhanced both computational efficiency and accuracy. Characters with a similarity score ranging from 0.9 to 1 were identified and applied based on the NOTAM dataset, thereby improving the accuracy of text error correction.https://ieeexplore.ieee.org/document/11029232/Aviation systemsbidirectional encoder representations from transformersChinese NOTAM text error correctiondeep learningknowledge graph |
| spellingShingle | Bing Dong Haoran Yao Chuang Luo Ruichao Yang Ziyue Wang Graph-Enhanced Deep Learning With Character Similarity Mining for Automated NOTAM Correction in Aviation Systems IEEE Access Aviation systems bidirectional encoder representations from transformers Chinese NOTAM text error correction deep learning knowledge graph |
| title | Graph-Enhanced Deep Learning With Character Similarity Mining for Automated NOTAM Correction in Aviation Systems |
| title_full | Graph-Enhanced Deep Learning With Character Similarity Mining for Automated NOTAM Correction in Aviation Systems |
| title_fullStr | Graph-Enhanced Deep Learning With Character Similarity Mining for Automated NOTAM Correction in Aviation Systems |
| title_full_unstemmed | Graph-Enhanced Deep Learning With Character Similarity Mining for Automated NOTAM Correction in Aviation Systems |
| title_short | Graph-Enhanced Deep Learning With Character Similarity Mining for Automated NOTAM Correction in Aviation Systems |
| title_sort | graph enhanced deep learning with character similarity mining for automated notam correction in aviation systems |
| topic | Aviation systems bidirectional encoder representations from transformers Chinese NOTAM text error correction deep learning knowledge graph |
| url | https://ieeexplore.ieee.org/document/11029232/ |
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