A knowledge extraction method for tracing quality defects in multi-source steel products based on text

To address the low efficiency in tracing defects of traditional steel products and the poor adaptability of existing knowledge extraction methods, this paper proposes a knowledge extraction approach for multi-source steel product quality defect tracing texts. First, a multi-source integrated text dataset for steel product quality defect tracing was constructed; second, a knowledge extraction model for steel product quality defect tracing based on a pipeline architecture was designed to accurately extract triple-structured knowledge from domain-specific texts; finally, named entity recognition experiments and knowledge extraction model experiments were carried out to systematically validate the effectiveness of the model, and the extracted structured knowledge was presented in the form of a knowledge graph. The results indicate that the proposed knowledge extraction method can provide technical support for accurate and efficient defect tracing of steel products.