arrow
返回

Knowledge graph with machine learning for product design

delete2022-01-01
delete22
PRE
AI
刘昂 封面图
刘昂 (Ang Liu) *
D
Dawen Zhang
王宇琛 封面图
王宇琛 (Yuchen Wang)
X
Xiwei Xu
DOI:10.1016/j.cirp.2022.03.025delete
delete原文链接
delete原文求助
delete分享
delete收藏
摘要

摘要

En 中文
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. (c) 2022 CIRP. Published by Elsevier Ltd. All rights reserved.
Keyword:
Machine learning
knowledge graph
product design

期刊

C
CIRP Annals and Manufacturing Technology
IF:
3.6
论文数:
3.4K
被引数:
1.3W

机构

C
引用论文

引用论文

The evolution, challenges, and future of knowledge representation in product design systems
err2013-02-01
err517
PREAI
errChandrasegaran, Senthil K.; Ramani, Karthik; Sriram, Ram D.; Horvath, Imre; Bernard, Alain; Harik, Ramy F.; Gao, Wei
err分享
err收藏
学者 查看更多内容