Design Knowledge Representation with Technology Semantic Network
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Abstract (arXiv)
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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Dec 2020.
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Citing papers per year: 2021: 4; 2022: 1.
5 papers.
- 2021Data-Driven Design-by-Analogy: State of the Art and Future Directionscited by 18
- 2021Engineering Knowledge Graph from Patent Databasecited by 18
- 2021Natural Language Processing in-and-for Design Researchcited by 15
- 2021Patent Data for Engineering Design: A Critical Review and Future Directionscited by 5
- 2022WikiLink: an encyclopedia-based semantic network for design innovationcited by 1
- 2022WikiLink: an encyclopedia-based semantic network for design innovation30 Aug 2022
- 2021Natural Language Processing in-and-for Design Research27 Nov 2021
- 2021Patent Data for Engineering Design: A Critical Review and Future Directions15 Nov 2021
- 2021Engineering Knowledge Graph from Patent Database12 Jun 2021
- 2021Data-Driven Design-by-Analogy: State of the Art and Future Directions3 Jun 2021