Knowledge graph with machine learning for product design

Ang Liu*, Dawen Zhang, Yuchen Wang, Xiwei Xu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)117-120
Number of pages4
JournalCIRP Annals
Volume71
Issue number1
DOIs
Publication statusPublished - Jan 2022
Externally publishedYes

Keywords

  • knowledge graph
  • Machine learning
  • product design

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