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Towards the extraction of semantic relations in design with natural language processing

Published online by Cambridge University Press:  16 May 2024

Vito Giordano ,
Marco Consoloni ,
Filippo Chiarello  and
Gualtiero Fantoni
Vito Giordano*
Affiliation:
University of Pisa, Italy Business Engineering for Data Science Lab (B4DS), Italy
Marco Consoloni
Affiliation:
University of Pisa, Italy Business Engineering for Data Science Lab (B4DS), Italy
Filippo Chiarello
Affiliation:
University of Pisa, Italy Business Engineering for Data Science Lab (B4DS), Italy
Gualtiero Fantoni
Affiliation:
University of Pisa, Italy Business Engineering for Data Science Lab (B4DS), Italy
*
[email protected]

Abstract

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Natural Language Processing (NLP) has been extensively applied in design, particularly for analyzing technical documents like patents and scientific papers to identify entities such as functions, technical feature, and problems. However, there has been less focus on understanding semantic relations within literature, and a comprehensive definition of what constitutes a relation is still lacking. In this paper, we define relation in the context of design and the fundamental concepts linked to it. Subsequently, we introduce a framework for employing NLP to extract relations relevant to design.

Keywords

large language model (LLM)artificial intelligence (AI)design cognitionnatural language processing (NLP)
Type
Artificial Intelligence and Data-Driven Design
Information
Proceedings of the Design Society , Volume 4: DESIGN 2024 , May 2024 , pp. 2059 - 2068
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), 2024.

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