MBJELEL: An End-to-End Knowledge Graph Entity Linking Method Applied to Civil Aviation Emergencies

• Published in: International journal of computational intelligence systems Vol. 17; no. 1; pp. 1 - 15
• Main Authors: Qu, Jiayi, Wang, Jintao, Zhao, Zuyi, Chen, Xingguo
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 10.09.2024; Springer
• Subjects: Artificial Intelligence; Civil aviation emergencies; Computational Intelligence; Control; End-to-end federated coding; Engineering; Entity linking; Mathematical Logic and Foundations; Mechatronics; Research Article; Robotics; Entity linking; Civil aviation emergencies; End-to-end federated coding
• ISSN: 1875-6883; 1875-6883
• DOI: 10.1007/s44196-024-00647-w

Aviation emergency management is playing a more and more important role in the aviation field. How to make effective use of massive heterogeneous and multi-source aviation accident knowledge has become a great challenge for aviation emergency management. Aiming at the problems such as too long physical length, mixed and composite entities, similar character of domain entity names, information difference between entities, separation of codes between entities, coding errors during transmission, etc., the construction method of knowledge map of civil aviation emergencies is studied. In previous research methods, entity link is always divided into two parts, that is, first detection and then disambiguation, which makes the mentioned entity and the candidate entity are encoded separately, and there is error transmission between the two parts, modules cannot communicate with each other, and the close association between entities cannot be well learned. In this paper, we proposed an end-to-end entity linking method based on two-layer BiLSTM model joint coding vectorize each word of civil aviation text information, and then concatenate feature vectors into two-layer BiLSTM model to obtain high-level context representation. Because the joint encoding of boundary information can reduce the error transmission, information is exchanged between candidate entities during the initial encoding to enhance the closeness between candidate entities and candidate entities. The experimental results show that compared with other sota models, the F1 value of the proposed model reaches 88.97%.