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<p>With the frequent use of hazardous chemicals in industrial production and experimental research, their recovery and hazard characterization are very important for the protection of environmental health and sustainable development. However, it is not efficient enough to analyze and judge the hazardous chemicals in the form of a table query, so the introduction of big data-related technologies is of great importance. To gain a better understanding of the characteristics and degree of hazard associated with chemicals, this paper incorporates the hazardous chemicals domain knowledge graph into the process of identifying hazardous chemical risks. A hazardous chemical recovery text recognition model, KG-TextRCNN, is constructed to minimize errors resulting from subjective evaluations or manual recognition. The experimental results indicate that the hazardous chemicals text corpus recognition has a total average precision value of 99.30%, with recall and F1 values above 99%. During the classification step of max pooling, the model achieved average precision, recall, and F1 values of 85.19%, 86.29%, and 85.27%, respectively. The model’s overall performance is stable, with high precision and recall rates for most categories. This paper conducts ablation and comparison experiments to evaluate and validate the effectiveness of the model.</p>

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A text feature extraction model for hazardous chemical recovery identification and attribute classification embedded in domain knowledge graph

  • 期刊论文

  • 发表日期：

    2025年3月17日

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  • Quan Cheng,
  • Shuangbao Zhang,
  • Lanyu Yang

摘要

With the frequent use of hazardous chemicals in industrial production and experimental research, their recovery and hazard characterization are very important for the protection of environmental health and sustainable development. However, it is not efficient enough to analyze and judge the hazardous chemicals in the form of a table query, so the introduction of big data-related technologies is of great importance. To gain a better understanding of the characteristics and degree of hazard associated with chemicals, this paper incorporates the hazardous chemicals domain knowledge graph into the process of identifying hazardous chemical risks. A hazardous chemical recovery text recognition model, KG-TextRCNN, is constructed to minimize errors resulting from subjective evaluations or manual recognition. The experimental results indicate that the hazardous chemicals text corpus recognition has a total average precision value of 99.30%, with recall and F1 values above 99%. During the classification step of max pooling, the model achieved average precision, recall, and F1 values of 85.19%, 86.29%, and 85.27%, respectively. The model’s overall performance is stable, with high precision and recall rates for most categories. This paper conducts ablation and comparison experiments to evaluate and validate the effectiveness of the model.