{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T04:02:53Z","timestamp":1747108973462,"version":"3.40.5"},"reference-count":25,"publisher":"American Institute of Aeronautics and Astronautics (AIAA)","issue":"11","funder":[{"name":"Supported by the Central University Basic Research Funding Project","award":["J2023-050"],"award-info":[{"award-number":["J2023-050"]}]},{"name":"The project was funded by the Key scientific Research Project of Civil Aviation Flight College of China","award":["ZJ2021-09"],"award-info":[{"award-number":["ZJ2021-09"]}]}],"content-domain":{"domain":["arc.aiaa.org"],"crossmark-restriction":true},"short-container-title":["Journal of Aerospace Information Systems"],"published-print":{"date-parts":[[2024,11,1]]},"abstract":"<jats:p> Notice to airmen (NOTAMs) constitutes a vital element in civil aviation operational intelligence. Historically, the processing of these notices has been manual. However, with the significant increase in the number of NOTAMs, issues including low efficiency, time-consuming processes, and high error rates associated with manual processing have become apparent. To address these challenges, we propose an enhanced approach utilizing the Bi-GRU-CRF-Attention model, based on a dataset of 105,797 NOTAMs collected from the Intelligence Center between September 2020 and April 2023. In this methodology, we employ preprocessing techniques to train the model using processed NOTAMs. Subsequently, the trained model is utilized for named entity recognition, identifying entities within the notices, such as status, facilities, and reasons and segmenting sentences into words. Following this, an advanced BERT-DPCNN method is employed to classify the identified entities, yielding triplets comprising NOTAM entities, their categories, and corresponding processing methods. By integrating rule-based approaches, we configure a NOTAM knowledge graph using neo4j. This process establishes an automated NOTAM processing system. This system can autonomously determine the category of a NOTAM upon reception and utilize the Cypher language to query for the appropriate processing method. <\/jats:p>","DOI":"10.2514\/1.i011416","type":"journal-article","created":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T06:29:56Z","timestamp":1721024996000},"page":"906-913","update-policy":"https:\/\/doi.org\/10.2514\/aiaa_crossmarkpolicy","source":"Crossref","is-referenced-by-count":0,"title":["Automated Processing Method for Chinese NOTAMs Based on Knowledge Graph"],"prefix":"10.2514","volume":"21","author":[{"given":"Bing","family":"Dong","sequence":"first","affiliation":[{"name":"Civil Aviation Flight University of China"}]},{"given":"Chuang","family":"Luo","sequence":"additional","affiliation":[{"name":"Civil Aviation Flight University of China"}]},{"given":"Kuangong","family":"Hao","sequence":"additional","affiliation":[{"name":"Civil Aviation Flight University of China"}]},{"given":"Anquan","family":"Liu","sequence":"additional","affiliation":[{"name":"Civil Aviation Flight University of China"}]},{"given":"Xinqian","family":"Li","sequence":"additional","affiliation":[{"name":"Civil Aviation Flight University of China"}]}],"member":"1387","reference":[{"key":"r1","unstructured":"XuZ. \u201cDesign and Realization of Navigation Notice Management System for Southwest Air Traffic Control Administration (SWATCA),\u201d Ph.D. Dissertation, Univ. of Electronic Science and Technology of China, Chengdu, China, 2015."},{"issue":"2","key":"r2","first-page":"14","volume":"40","author":"Heng X.","year":"2022","journal-title":"Journal of the Chinese Civil Avian University"},{"issue":"4","key":"r3","first-page":"6","volume":"6","author":"Li H.","year":"2022","journal-title":"Journal of Civil 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Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/arc.aiaa.org\/doi\/pdf\/10.2514\/1.I011416","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,12]],"date-time":"2025-05-12T12:42:34Z","timestamp":1747053754000},"score":1,"resource":{"primary":{"URL":"https:\/\/arc.aiaa.org\/doi\/10.2514\/1.I011416"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,1]]},"references-count":25,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2024,11]]}},"alternative-id":["10.2514\/1.I011416"],"URL":"https:\/\/doi.org\/10.2514\/1.i011416","relation":{},"ISSN":["1940-3151","2327-3097"],"issn-type":[{"type":"print","value":"1940-3151"},{"type":"electronic","value":"2327-3097"}],"subject":[],"published":{"date-parts":[[2024,11,1]]},"assertion":[{"value":"2023-12-22","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication 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