Design knowledge graph-aided conceptual product design approach based on joint entity and relation extraction

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 De...

Full description

Saved in:
Bibliographic Details
Published in:Journal of intelligent & fuzzy systems 2023-01, Vol.44 (3), p.5333-5355
Main Authors: Huang, Yuexin, Yu, Suihuai, Chu, Jianjie, Su, Zhaojing, Zhu, Yaokang, Wang, Hanyu, Wang, Mengcheng, Fan, Hao
Format: Article
Language:English
Subjects:
Algorithms
Knowledge acquisition
Knowledge based engineering
Knowledge representation
Machine learning
Porcelain
Product design
Product development
Unstructured data
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1064-1246
1875-8967
DOI:10.3233/JIFS-223100