Enhancing novel product iteration: An integrated framework for heuristic ideation via interpretable conceptual design knowledge graph

作者：Yangfan Cong, Suihuai Yu, Jianjie Chu, Yuexin Huang, Ning Ding, Cong Fang, Stephen Jia Wang · 发表于：Advanced Engineering Informatics · 年份：2025 · DOI：10.1016/j.aei.2025.103131 · 被引用次数：8 · 研究领域：Design Education and Practice、Knowledge Management and Technology、Technology Assessment and Management

• The study emphasizes knowledge graph-powered product iteration within an under-explored NPD domain of newer and less-established novel products. • An interpretable conceptual design knowledge graph (I-CDKG) is constructed to facilitate designers in generating innovative and cost-effective heuristic product ideations. • A hybrid method combining deep-learning ERNIE-BiGRU-CRF model, BIESO labeling mode, and triple-extracting algorithm is proposed to facilitate the I-CDKG construction. • The I-CDKG boasts both inherent and acquired interpretability reinforced by a Cluster-Relation-Nest organizational strategy for the intuitive locating of design knowledge. Novel products emerge over time to survive the competitive landscape as no existing product can perpetually satisfy all evolving customer expectations. These products are often characterized by groundbreaking solutions previously unavailable on the market. However, the swift imitation of successful novel products by competitors underscores the need for sustained iteration and continuous improvement. Designers increasingly face challenges in keeping up to date with the growing volume and fragmented nature of design information from diverse sources. While knowledge graphs show promise in structuring and organizing complex design information, their effective application in the ideation process remains limited due to difficulties in automatic knowledge extraction and the lack of interpretability aligned well with designers’ cogn...