Hostname: page-component-cd9895bd7-gxg78 Total loading time: 0 Render date: 2024-12-23T17:29:19.532Z Has data issue: false hasContentIssue false
  • English
  • Français

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

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

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.

References

Altshuller, G. S. (1984). "Creativity as an exact science: the theory of the solution of inventive problems".CrossRefGoogle Scholar
An, J., Kim, K., Mortara, L., & Lee, S. (2018). "Deriving technology intelligence from patents: Preposition-based semantic analysis". Journal of Informetrics, 12(1), 217-236. https://doi.org/10.1016/j.joi.2018.01.001CrossRefGoogle Scholar
An, X., Li, J., Xu, S., Chen, L., & Sun, W. (2021). "An improved patent similarity measurement based on entities and semantic relations". Journal of Informetrics, 15(2), 101135. https://doi.org/10.1016/j.joi.2021.101135CrossRefGoogle Scholar
Chakrabarti, A., Sarkar, P., Leelavathamma, B., & Nataraju, B. S. (2005). "A functional representation for aiding biomimetic and artificial inspiration of new ideas". AI EDAM, 19(2), 113-132. https://doi.org/10.1017/S0890060405050109Google Scholar
Chen, L., Xu, S., Zhu, L., Zhang, J., Lei, X., & Yang, G. (2020). "A deep learning based method for extracting semantic information from patent documents". Scientometrics, 125, 289-312. https://doi.org/10.1007/s11192-020-03634-yCrossRefGoogle Scholar
Chen, L., Xu, S., Zhu, L., Zhang, J., Yang, G., & Xu, H. (2022). "A deep learning based method benefiting from characteristics of patents for semantic relation classification". Journal of Informetrics, 16(3), 101312. https://doi.org/10.1016/j.joi.2022.101312CrossRefGoogle Scholar
Chiarello, F., Cirri, I., Melluso, N., Fantoni, G., Bonaccorsi, A., & Pavanello, T. (2019). "Approaches to automatically extract affordances from patents". In Proceedings of the Design Society: International Conference on Engineering Design (Vol. 1, No. 1, pp. 2487-2496). Cambridge University Press. https://doi.org/10.1017/dsi.2019.255Google Scholar
Chiarello, F., Bonaccorsi, A., & Fantoni, G. (2020). "Technical sentiment analysis. Measuring advantages and drawbacks of new products using social media". Computers in Industry, 123, 103299. https://doi.org/10.1016/j.compind.2020.103299CrossRefGoogle Scholar
Chiarello, F., Belingheri, P., & Fantoni, G. (2021). "Data science for engineering design: State of the art and future directions". Computers in Industry, 129, 103447. https://doi.org/10.1016/j.compind.2021.103447CrossRefGoogle Scholar
Choi, S., Park, H., Kang, D., Lee, J. Y., & Kim, K. (2012). "An SAO-based text mining approach to building a technology tree for technology planning". Expert Systems with Applications, 39(13), 11443-11455. https://doi.org/10.1016/j.eswa.2012.04.014CrossRefGoogle Scholar
Choi, S., Kim, H., Yoon, J., Kim, K., & Lee, J. Y. (2013). "An SAO-based text-mining approach for technology roadmapping using patent information". R&D Management, 43(1), 52-74.Google Scholar
Detroja, K., Bhensdadia, C. K., & Bhatt, B. S. (2023). "A Survey on Relation Extraction". Intelligent Systems with Applications, 200244. https://doi.org/10.1016/j.iswa.2023.200244CrossRefGoogle Scholar
Fantoni, G., Apreda, R., Gabelloni, D., & Bonaccorsi, A. (2011). "Do functions exist?". In DS 68-2: Proceedings of the 18th International Conference on Engineering Design (ICED 11), Impacting Society through Engineering Design, Vol. 2: Design Theory and Research Methodology, Lyngby/Copenhagen, Denmark, 15.-19.08. 2011 (pp. 304-313).Google Scholar
Fantoni, G., Apreda, R., Dell'Orletta, F., & Monge, M. (2013). "Automatic extraction of function–behaviour–state information from patents". Advanced Engineering Informatics, 27(3), 317-334. https://doi.org/10.1016/j.aei.2013.04.004CrossRefGoogle Scholar
Gero, J. S., & Kannengiesser, U. (2004). "The situated function–behaviour–structure framework". Design studies, 25(4), 373-391. https://doi.org/10.1016/j.destud.2003.10.010CrossRefGoogle Scholar
Giordano, V., Puccetti, G., Chiarello, F., Pavanello, T., & Fantoni, G. (2023). "Unveiling the inventive process from patents by extracting problems, solutions and advantages with natural language processing". Expert Systems with Applications, 229, 120499. https://doi.org/10.1016/j.eswa.2023.120499CrossRefGoogle Scholar
Giordano, V., Chiarello, F., Fantoni, G. (2024)."How Engineering Design can Enhance Tech Mining: a Bibliometric and Systematic Literature Review". Technological Forecasting and Social Change [Second Round Review]Google Scholar
Guarino, G., Samet, A., & Cavallucci, D. (2022). "PaTRIZ: A framework for mining TRIZ contradictions in patents". Expert Systems with Applications, 207, 117942. https://doi.org/10.1016/j.eswa.2022.117942CrossRefGoogle Scholar
Guo, J., Wang, X., Li, Q., & Zhu, D. (2016). "Subject–action–object-based morphology analysis for determining the direction of technological change". Technological Forecasting and Social Change, 105, 27-40. https://doi.org/10.1016/j.techfore.2016.01.028CrossRefGoogle Scholar
Hatchuel, A., & Weil, B. (2009). "CK design theory: an advanced formulation". Research in engineering design, 19, 181-192. https://doi.org/10.1007/s00163-008-0043-4CrossRefGoogle Scholar
Han, Y., & Moghaddam, M. (2021). "Eliciting attribute-level user needs from online reviews with deep language models and information extraction". Journal of Mechanical Design, 143(6), 061403. https://doi.org/10.1115/1.4048819CrossRefGoogle Scholar
Han, J., Sarica, S., Shi, F., & Luo, J. (2022). "Semantic networks for engineering design: state of the art and future directions". Journal of Mechanical Design, 144(2), 020802. https://doi.org/10.1115/1.4052148Google Scholar
Jang, H. and Yoon, B., (2021). "TechWordNet: Development of semantic relation for technology information analysis using F-term and natural language processing". Information Processing & Management, 58(6), p.102752. https://doi.org/10.1016/j.ipm.2021.102752CrossRefGoogle Scholar
Jeong, C., & Kim, K. (2014). "Creating patents on the new technology using analogy-based patent mining". Expert Systems with Applications, 41(8), 3605-3614. https://doi.org/10.1016/j.eswa.2013.11.045CrossRefGoogle Scholar
Jiang, P., Atherton, M., & Sorce, S. (2023). "Extraction and linking of motivation, specification and structure of inventions for early design use". Journal of Engineering Design, 1-26. https://doi.org/10.1080/09544828.2023.2227934CrossRefGoogle Scholar
Kim, H., & Kim, K. (2012). "Causality-based function network for identifying technological analogy". Expert Systems with Applications, 39(12), 10607-10619. https://doi.org/10.1016/j.eswa.2012.02.156CrossRefGoogle Scholar
Kim, H., Joung, J., & Kim, K. (2018). "Semi-automatic extraction of technological causality from patents". Computers & Industrial Engineering, 115, 532-542. https://doi.org/10.1016/j.cie.2017.12.004CrossRefGoogle Scholar
Melluso, N., Pardelli, S., Fantoni, G., Chiarello, F., & Bonaccorsi, A. (2021). "Detecting bad design and bias from patents". Proceedings of the Design Society, 1, 1173-1182. https://doi.org/10.1017/pds.2021.117CrossRefGoogle Scholar
Pahl, G., Beitz, W., Feldhusen, J., & Grote, K. H. (2007). Engineering Design: A Systematic Approach. Engineering Design: A Systematic Approach.CrossRefGoogle Scholar
Russo, D., & Montecchi, T. (2011). "A function-behaviour oriented search for patent digging". In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 54792, pp. 1111-1120).Google Scholar
Sarica, S., Song, B., Luo, J., & Wood, K. L. (2021). "Idea generation with technology semantic network". AI EDAM, 35(3), 265-283. https://doi.org/10.1017/S0890060421000020Google Scholar
Sarica, S., Han, J., & Luo, J. (2023). "Design representation as semantic networks". Computers in Industry, 144, 103791. https://doi.org/10.1016/j.compind.2022.103791CrossRefGoogle Scholar
Sasajima, M., Kitamura, Y., Ikeda, M., & Mizoguchi, R. (1995). "FBRL: A function and behavior representation language". In IJCAI (Vol. 95, pp. 1830-1836).Google Scholar
Siddharth, L., Blessing, L., & Luo, J. (2022a). "Natural language processing in-and-for design research". Design Science, 8, e21. https://doi.org/10.1017/dsj.2022.16CrossRefGoogle Scholar
Siddharth, L., Blessing, L. T., Wood, K. L., & Luo, J. (2022b). "Engineering knowledge graph from patent database". Journal of Computing and Information Science in Engineering, 22(2), 021008. https://doi.org/10.3390/pr11102831CrossRefGoogle Scholar
Suh, N. P. (1998). "Axiomatic design theory for systems". Research in engineering design, 10, 189-209. https://doi.org/10.1007/s001639870001CrossRefGoogle Scholar
Sun, Q., Xu, T., Zhang, K., Huang, K., Lv, L., Li, X., & Dore-Natteh, …, D. (2022a). "Dual-Channel and Hierarchical Graph Convolutional Networks for document-level relation extraction". Expert Systems with Applications, 205, 117678. https://doi.org/10.1016/j.eswa.2022.117678CrossRefGoogle Scholar
Sun, Y., Liu, W., Cao, G., Peng, Q., Gu, J., & Fu, J. (2022b). "Effective design knowledge abstraction from Chinese patents based on a meta-model of the patent design knowledge graph". Computers in Industry, 142, 103749. https://doi.org/10.1016/j.compind.2022.103749CrossRefGoogle Scholar
Yoon, B., & Park, Y. (2005). "A systematic approach for identifying technology opportunities: Keyword-based morphology analysis". Technological Forecasting and Social Change, 72(2), 145-160. https://doi.org/10.1016/j.techfore.2004.08.011CrossRefGoogle Scholar
Yoon, J., & Kim, K. (2012). "An analysis of property–function based patent networks for strategic R&D planning in fast-moving industries: The case of silicon-based thin film solar cells". Expert Systems with Applications, 39(9), 7709-7717. https://doi.org/10.1016/j.eswa.2012.01.035CrossRefGoogle Scholar