DGAD: knowledge extraction for spindle assembly graph construction in winding machines

This paper presents a knowledge graph construction method for spindle assembly in winding machines, aiming to address issues of dispersed knowledge and underutilization during the assembly process. To overcome the complexities of domain-specific knowledge and the challenges of relational triple extraction, the authors propose a comprehensive framework that includes knowledge modeling, knowledge extraction, and visualization. First, an ontology library tailored to the spindle assembly domain is developed, defining relevant entity types and relationship types. Then, the authors propose an enhanced extraction model, DGAD, which utilizes dual-gated dynamic convolution and multi-head attention mechanisms to automate the extraction of entities and relationships, effectively integrating local context and global features. The extracted triples are visualized using the Neo4j database, helping users intuitively understand the relationships between entities in the assembly process. Experimental results show that the proposed model achieves F1 score improvements of 5.83% and 7.03% in named entity recognition and relationship extraction tasks, outperforming baseline methods. The visualization of the knowledge graph provides a solid foundation for downstream applications, such as intelligent question-answering systems and fault diagnosis.