A hazardous chemical knowledge base construction method based on knowledge graph. (25th October 2022)

• Record Type: Journal Article
• Authors: Chen, Guanlin; Hu, Qiao; Lu, Qi; Li, Kaimin; Zhu, Bangjie
• Is Part Of: International journal of reasoning-based intelligent systems. Volume 14:Number 4(2022)
• Page Start: 184
• Page End: 193
• Publication Date: 2022-10-25
• Subjects: hazardous chemicals -- knowledge graph -- NLP -- NER -- sensor; Artificial intelligence -- Periodicals; Reasoning -- Periodicals; 006.3
• Languages: English
• ISSNs: 1755-0556

Abstract

This paper proposes a model for extracting risk information of hazardous chemicals and constructs a hazardous chemicals knowledge graph. This paper constructs a hazardous chemicals risk information dataset. In this paper, the proposed model combines word-feature into character-feature as character's embedding; uses a joint model of BiLSTM and self-attention mechanism to encode characters and a bidirectional label distribution transfer model is used to decode the classification. Using basic data and the risk information extracted by the model, this paper establishes a knowledge graph. The experimental results show that the model has a better effect on the extraction of risk information than classical models, and the knowledge graph is also more comprehensive in the knowledge system. Combined with sensors, the management system based on this knowledge graph can determine whether a chemical in a warehouse is in a safe environment and whether a chemical meets the storage conditions.