Affiliations: [a] Key Laboratory of Industrial Design and Ergonomics, Ministry of Industry and Information Technology, Northwestern Polytechnical University, Xi’an, China
| [b] School of Industrial Design Engineering, Delft University of Technology, Delft, The Netherlands
| [c] Department of Industrial Design, College of Arts, Shandong University of Science and Technology, Tsingtao, China
| [d] School of Computer Science and Technology, East China Normal University, Shanghai, China
Abstract: Design knowledge is critical to creating ideas in the conceptual design stage of product development for innovation. Fragmentary design data, massive multidisciplinary knowledge call for the development of a novel knowledge acquisition approach for conceptual product design. This study proposes a Design Knowledge Graph-aided (DKG-aided) conceptual product design approach for knowledge acquisition and design process improvement. The DKG framework uses a deep-learning algorithm to discover design-related knowledge from massive fragmentary data and constructs a knowledge graph for conceptual product design. The joint entity and relation extraction model is proposed to automatically extract design knowledge from massive unstructured data. The feasibility and high accuracy of the proposed design knowledge extraction model were demonstrated with experimental comparisons and the validation of the DKG in the case study of conceptual product design inspired by massive real data of porcelain.
Keywords: Conceptual product design, design knowledge graph, deep learning, knowledge acquisition, joint entity and relation extraction
