Named entity recognition in aviation products domain based on BERT

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dc.contributor.authorYang, Mingye
dc.contributor.authorNamoano, Bernadin
dc.contributor.authorFarsi, Maryam
dc.contributor.authorAhmet Erkoyuncu, John
dc.date.accessioned2025-01-07T15:34:24Z
dc.date.available2025-01-07T15:34:24Z
dc.date.freetoread2025-01-07
dc.date.issued2024-12-12
dc.date.pubOnline2024-12-12
dc.description.abstractThe aviation products' manufacturing industry is undergoing a profound transformation towards intelligence, among which the construction of a knowledge graph specifically for the aviation field has become the core link in achieving cognitive intelligence. In the process of knowledge graph construction, named entity recognition (NER) is a key step and one of the main tasks of knowledge extraction. Given the high degree of specialisation of aviation product text data and the wide span of contextual information, existing models often perform poorly in entity extraction. This paper proposes a new Named Entity Recognition (NER) method specifically tailored for the aviation product field (BBC-Ap), introducing an innovative approach that leverages domain-specific ontologies and advanced deep learning algorithms to significantly enhance the accuracy and efficiency of entity extraction from complex technical documents. The first step of this method is to establish an ontology model of aviation products and annotate the relevant text data to form a dataset for training the named entity model. Next, it adopts a multi-level model structure based on BERT, in which BERT is used to generate word vector representations, a bidirectional long short-term memory network (BiLSTM) is used as an encoder to extract semantic features, and a conditional random field (CRF) is used as a decoder to achieve optimal label assignment. Through experiments on the constructed aviation product dataset, the model achieved a Precision value of 91.74%, a Recall value of 92.46%, and an F1 score of 92.1%, Compared with other baseline models, the F1-score is improved by 0.9% to 1.5%. At the same time, the model also performs well on standard datasets such as CoNLLpp, with a Precision value of 92.87%, a Recall value of 92.54%, and an F1-Score of 92.70%. Finally, the model was used to successfully construct a knowledge graph reflecting the relationships between aviation products in Neo4j, further demonstrating the effectiveness and practicality of the method.
dc.description.journalNameIEEE Access
dc.description.sponsorshipEngineering and Physical Sciences Research Council
dc.description.sponsorshipEngineering and Physical Sciences Research Council (EPSRC), Grant Number: EP/Z533221/1
dc.format.extentpp. 189710-189721
dc.identifier.citationYang M, Namoano B, Farsi M, Ahmet Erkoyuncu J. (2024) Named entity recognition in aviation products domain based on BERT. IEEE Access, Volume 12, December 2024, pp. 189710-189721
dc.identifier.eissn2169-3536
dc.identifier.elementsID560692
dc.identifier.issn2169-3536
dc.identifier.urihttps://doi.org/10.1109/access.2024.3516390
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23325
dc.identifier.volumeNo12
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.publisher.urihttps://ieeexplore.ieee.org/document/10795123
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject4605 Data Management and Data Science
dc.subject46 Information and Computing Sciences
dc.subject4602 Artificial Intelligence
dc.subject4611 Machine Learning
dc.subjectNetworking and Information Technology R&D (NITRD)
dc.subject40 Engineering
dc.subject46 Information and computing sciences
dc.titleNamed entity recognition in aviation products domain based on BERT
dc.typeArticle
dc.type.subtypeJournal Article
dcterms.dateAccepted2024-12-06

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