Dynamic knowledge modeling and fusion method for custom apparel production process based on knowledge graph. (January 2023)

• Record Type: Journal Article
• Authors: Shen, Xingwang; Li, Xinyu; Zhou, Bin; Jiang, Yanan; Bao, Jinsong
• Is Part Of: Advanced engineering informatics. Volume 55(2023)
• Publication Date: 2023-01
• Subjects: Knowledge graph -- Custom apparel -- Knowledge modeling -- Knowledge fusion -- Knowledge generation; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2023.101880
• Languages: English
• ISSNs: 1474-0346

Abstract

Abstract: Compared with ordinary mass-produced apparel products, custom apparel products will generate more data at each stage of their life cycle. Such data is in a highly dynamic state, while the relationship between the data is more complex. However, the current use of traditional relational data to store the whole life cycle data of custom apparel products has several problems of high redundancy, weak correlation, discrete distribution, and certain limitation of storage capacity. Therefore, based on knowledge graph, a dynamic knowledge modeling and fusion method is proposed for the production process of custom apparel. Firstly, an ontology-based knowledge modeling method is designed for custom apparel, which defined three types of ontology modeling methods for the process, resources, and features. On this basis, a knowledge graph construction method based on bi-directional fusion for the custom apparel production system is proposed. With one order as a unit, a knowledge graph facet (KGF) model, as well as the derived knowledge representation, generation and fusion method, is established to realize dynamic knowledge fusion of the custom apparel production process. Finally, taking the suit production process of a custom apparel factory as an example, the corresponding knowledge graph is constructed based on the ontology knowledge model, and the effectiveness of the proposed knowledge fusion method is verified.