LLM-ACNC: Aerospace Requirement Texts Knowledge Graph Construction Utilizing Large Language Model
Traditional methods for requirement identification depend on the manual transformation of unstructured requirement texts into formal documents, a process that is both inefficient and prone to errors. Although requirement knowledge graphs offer structured representations, current named entity recogni...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/12/6/463 |
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| _version_ | 1849423871235588096 |
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| author | Yuhao Liu Junjie Hou Yuxuan Chen Jie Jin Wenyue Wang |
| author_facet | Yuhao Liu Junjie Hou Yuxuan Chen Jie Jin Wenyue Wang |
| author_sort | Yuhao Liu |
| collection | DOAJ |
| description | Traditional methods for requirement identification depend on the manual transformation of unstructured requirement texts into formal documents, a process that is both inefficient and prone to errors. Although requirement knowledge graphs offer structured representations, current named entity recognition and relation extraction techniques continue to face significant challenges in processing the specialized terminology and intricate sentence structures characteristic of the aerospace domain. To overcome these limitations, this study introduces a novel approach for constructing aerospace-specific requirement knowledge graphs using a large language model. The method first employs the GPT model for data augmentation, followed by BERTScore filtering to ensure data quality and consistency. An efficient continual learning based on token index encoding is then implemented, guiding the model to focus on key information and enhancing domain adaptability through fine-tuning of the Qwen2.5 (7B) model. Furthermore, a chain-of-thought reasoning framework is established for improved entity and relation recognition, coupled with a dynamic few-shot learning strategy that selects examples adaptively based on input characteristics. Experimental results validate the effectiveness of the proposed method, achieving F1 scores of 88.75% in NER and 89.48% in relation extraction tasks. |
| format | Article |
| id | doaj-art-36a641ade98f45579c381295acc1bb6b |
| institution | Kabale University |
| issn | 2226-4310 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj-art-36a641ade98f45579c381295acc1bb6b2025-08-20T03:30:25ZengMDPI AGAerospace2226-43102025-05-0112646310.3390/aerospace12060463LLM-ACNC: Aerospace Requirement Texts Knowledge Graph Construction Utilizing Large Language ModelYuhao Liu0Junjie Hou1Yuxuan Chen2Jie Jin3Wenyue Wang4China Aerospace Academy of Systems Science and Engineering, Beijing 100048, ChinaChina Aerospace Academy of Systems Science and Engineering, Beijing 100048, ChinaChina Aerospace Academy of Systems Science and Engineering, Beijing 100048, ChinaChina Aerospace Academy of Systems Science and Engineering, Beijing 100048, ChinaChina Aerospace Academy of Systems Science and Engineering, Beijing 100048, ChinaTraditional methods for requirement identification depend on the manual transformation of unstructured requirement texts into formal documents, a process that is both inefficient and prone to errors. Although requirement knowledge graphs offer structured representations, current named entity recognition and relation extraction techniques continue to face significant challenges in processing the specialized terminology and intricate sentence structures characteristic of the aerospace domain. To overcome these limitations, this study introduces a novel approach for constructing aerospace-specific requirement knowledge graphs using a large language model. The method first employs the GPT model for data augmentation, followed by BERTScore filtering to ensure data quality and consistency. An efficient continual learning based on token index encoding is then implemented, guiding the model to focus on key information and enhancing domain adaptability through fine-tuning of the Qwen2.5 (7B) model. Furthermore, a chain-of-thought reasoning framework is established for improved entity and relation recognition, coupled with a dynamic few-shot learning strategy that selects examples adaptively based on input characteristics. Experimental results validate the effectiveness of the proposed method, achieving F1 scores of 88.75% in NER and 89.48% in relation extraction tasks.https://www.mdpi.com/2226-4310/12/6/463system engineeringrequirement identificationlarge language modelknowledge graphnamed entity recognitionrelation extraction |
| spellingShingle | Yuhao Liu Junjie Hou Yuxuan Chen Jie Jin Wenyue Wang LLM-ACNC: Aerospace Requirement Texts Knowledge Graph Construction Utilizing Large Language Model Aerospace system engineering requirement identification large language model knowledge graph named entity recognition relation extraction |
| title | LLM-ACNC: Aerospace Requirement Texts Knowledge Graph Construction Utilizing Large Language Model |
| title_full | LLM-ACNC: Aerospace Requirement Texts Knowledge Graph Construction Utilizing Large Language Model |
| title_fullStr | LLM-ACNC: Aerospace Requirement Texts Knowledge Graph Construction Utilizing Large Language Model |
| title_full_unstemmed | LLM-ACNC: Aerospace Requirement Texts Knowledge Graph Construction Utilizing Large Language Model |
| title_short | LLM-ACNC: Aerospace Requirement Texts Knowledge Graph Construction Utilizing Large Language Model |
| title_sort | llm acnc aerospace requirement texts knowledge graph construction utilizing large language model |
| topic | system engineering requirement identification large language model knowledge graph named entity recognition relation extraction |
| url | https://www.mdpi.com/2226-4310/12/6/463 |
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