TY - JOUR
T1 - Knowledge graph with machine learning for product design
AU - Liu, Ang
AU - Zhang, Dawen
AU - Wang, Yuchen
AU - Xu, Xiwei
N1 - Publisher Copyright:
© 2022 CIRP
PY - 2022/1
Y1 - 2022/1
N2 - Knowledge graph is a particular form of graph that represents knowledge through entities and relations. Machine learning, particularly deep learning, can be adopted to construct, interpret, and enrich knowledge graph towards unknown entities and relations. As a knowledge-intensive endeavour, product design can greatly benefit from knowledge graph with machine learning. A structured framework is proposed to develop design-specific knowledge graph, based on which, deep learning is leveraged to learn graph embeddings, make predictions, and support reasoning. The framework effectiveness is validated through a quantitative experiment, where knowledge graph is used to make design-related predictions about smart products in home environment.
AB - Knowledge graph is a particular form of graph that represents knowledge through entities and relations. Machine learning, particularly deep learning, can be adopted to construct, interpret, and enrich knowledge graph towards unknown entities and relations. As a knowledge-intensive endeavour, product design can greatly benefit from knowledge graph with machine learning. A structured framework is proposed to develop design-specific knowledge graph, based on which, deep learning is leveraged to learn graph embeddings, make predictions, and support reasoning. The framework effectiveness is validated through a quantitative experiment, where knowledge graph is used to make design-related predictions about smart products in home environment.
KW - knowledge graph
KW - Machine learning
KW - product design
UR - http://www.scopus.com/inward/record.url?scp=85128998211&partnerID=8YFLogxK
U2 - 10.1016/j.cirp.2022.03.025
DO - 10.1016/j.cirp.2022.03.025
M3 - Article
AN - SCOPUS:85128998211
SN - 0007-8506
VL - 71
SP - 117
EP - 120
JO - CIRP Annals
JF - CIRP Annals
IS - 1
ER -