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DESIGN KNOWLEDGE REPRESENTATION WITH TECHNOLOGY SEMANTIC NETWORK

Published online by Cambridge University Press:  27 July 2021

Serhad Sarica  and
Jianxi Luo
Serhad Sarica*
Affiliation:
Engineering Product Development Pillar, Singapore University of Technology and Design
Jianxi Luo
Affiliation:
Engineering Product Development Pillar, Singapore University of Technology and Design
*
Sarica, Serhad, Singapore University of Technology and Design, Engineering Product Development, Singapore, [email protected]

Abstract

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Engineers often need to discover and learn designs from unfamiliar domains for inspiration or other particular uses. However, the complexity of the technical design descriptions and the unfamiliarity to the domain make it hard for engineers to comprehend the function, behavior, and structure of a design. To help engineers quickly understand a complex technical design description new to them, one approach is to represent it as a network graph of the design-related entities and their relations as an abstract summary of the design. While graph or network visualizations are widely adopted in the engineering design literature, the challenge remains in retrieving the design entities and deriving their relations. In this paper, we propose a network mapping method that is powered by Technology Semantic Network (TechNet). Through a case study, we showcase how TechNet’s unique characteristic of being trained on a large technology-related data source advantages itself over common-sense knowledge bases, such as WordNet and ConceptNet, for design knowledge representation.

Keywords

Semantic data processingDesign informaticsVisualisationTechnology Semantic NetworkKnowledge Representation
Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 1043 - 1052
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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