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SEMANTIC NETWORKS FOR ENGINEERING DESIGN: A SURVEY

Published online by Cambridge University Press:  27 July 2021

Ji Han ,
Serhad Sarica ,
Feng Shi  and
Jianxi Luo
Ji Han*
Affiliation:
University of Liverpool;
Serhad Sarica
Affiliation:
Singapore University of Technology and Design;
Feng Shi
Affiliation:
Amazon Web Services
Jianxi Luo
Affiliation:
Singapore University of Technology and Design;
*
Han, Ji, University of Liverpool, Industrial Design, United Kingdom, [email protected]

Abstract

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There have been growing uses of semantic networks in the past decade, such as leveraging large-scale pre-trained graph knowledge databases for various natural language processing (NLP) tasks in engineering design research. Therefore, the paper provides a survey of the research that has employed semantic networks in the engineering design research community. The survey reveals that engineering design researchers have primarily relied on WordNet, ConceptNet, and other common-sense semantic network databases trained on non-engineering data sources to develop methods or tools for engineering design. Meanwhile, there are emerging efforts to mine large scale technical publication and patent databases to construct engineering-contextualized semantic network databases, e.g., B-Link and TechNet, to support NLP in engineering design. On this basis, we recommend future research directions for the construction and applications of engineering-related semantic networks in engineering design research and practice.

Keywords

Semantic data processingKnowledge managementDesign informaticsSemantic networkKnowledge base
Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 2621 - 2630
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2021. Published by Cambridge University Press

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