Extraction of entity relationships serving the field of agriculture food safety regulation

• Published in: International journal of machine learning and cybernetics Vol. 15; no. 12; pp. 6077 - 6092
• Main Authors: Zhao, Zhihua, Liu, Yiming, Lv, Dongdong, Li, Ruixuan, Yu, Xudong, Mao, Dianhui
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2024; Springer Nature B.V
• Subjects: Agriculture; Algorithms; Artificial Intelligence; Big Data; Complex Systems; Computational Intelligence; Control; Deep learning; Engineering; Food safety; Mechatronics; Original Article; Pattern Recognition; Recognition; Regulation; Robotics; Safety standards; Semantics; Sparsity; Systems Biology; Relation extraction; Attention mechanisms; Bert; Agriculture food safety regulation; RCNN
• ISSN: 1868-8071; 1868-808X
• DOI: 10.1007/s13042-024-02304-2

Agriculture food (agri-food) safety is closely related to all aspects of people's lives. In recent years, with the emergence of deep learning technology based on big data, the extraction of information relations in the field of agri-food safety supervision has become a research hotspot. However, most of the current work only expands the relationship recognition based on the traditional named entity recognition task, which makes it difficult to establish a true 'connection' between entities and relationships. The pipelined and federated extraction architectures that have emerged in this area are problematic in practice. In addition, the contextual information of the text corpus in the agri-food safety regulatory domain has not been fully utilized. To address the above issues, this paper proposes a semi-joint entity relationship extraction model (EB-SJRE) based on contextual entity boundary features. Firstly, a Token pair subject-object correspondence matrix label is designed to intuitively model the subject-object boundary, which is more friendly to complex entities in the field of agri-food safety regulation. Secondly, the dynamic fine-tuning of Bert makes the text embedding more relevant to the textual context of the agri-food safety regulation domain. Finally, we introduce an attention mechanism in the Token pair tagging framework to capture deep semantic subject-object boundary association information, which cleverly solves the problem of bias exposure due to the pipeline structure and the dimensional explosion due to the joint extraction structure. The experimental results show that our model achieves the best F1-score of 88.71% on agri-food safety regulation domain data and F1-scores of 92.36%, 92.80%, 88.91%, and 92.21% on NYT, NYT-star, WebNLG, and WebNLG-star, respectively. This indicates that EB-SJRE has excellent generalization ability in both the agri-food safety regulatory and public sectors.