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A Text Mining Based Map of Engineering Design: Topics and their Trajectories Over Time

Part of: Methods of Design Research

Published online by Cambridge University Press:  26 July 2019

Filippo Chiarello ,
Nicola Melluso ,
Andrea Bonaccorsi  and
Gualtiero Fantoni

Abstract

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The Engineering Design field is growing fast and so is growing the number of sub-fields that are bringing value to researchers that are working in this context. From psychology to neurosciences, from mathematics to machine learning, everyday scholars and practitioners produce new knowledge of potential interest for designers.

This leads to complications in the researchers’ aims who want to quickly and easily find literature on a specific topic among a large number of scientific publications or want to effectively position a new research.

In the present paper, we address this problem by using state of the art text mining techniques on a large corpus of Engineering Design related documents. In particular, a topic modelling technique is applied to all the papers published in the ICED proceedings from 2003 to 2017 (3,129 documents) in order to find the main subtopics of Engineering Design. Finally, we analyzed the trends of these topics over time, to give a bird-eye view of how the Engineering Design field is evolving.

The results offer a clear and bottom-up picture of what Engineering design is and how the interest of researchers in different topics has changed over time.

Keywords

Semantic data processingBig dataMachine learningTopic Modelling
Type
Article
Information
Proceedings of the Design Society: International Conference on Engineering Design , Volume 1 , Issue 1 , July 2019 , pp. 2765 - 2774
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) 2019

References

Bonaccorsi, A., Apreda, R. and Fantoni, G. (2017), “Cognitive and Motivational Biases in Technology Foresight”, Technological Forecasting and Social Change (Under review).Google Scholar
Arun, R., Suresh, V., Madhavan, C. V. and Murthy, M. N. (2010), “On finding the natural number of topics with latent dirichlet allocation: Some observations”, In Pacific-Asia conference on knowledge discovery and data mining, (pp. 391–402), Springer, Berlin, Heidelberg.Google Scholar
Autrey, J. L., Sieber, J., Siddique, Z. and Mistree, F. (2018), “Leveraging Self-Assessment to Encourage Learning Through Reflection on Doing”, International Journal of Engineering Education, Vol. 34 No. 2, pp. 708–722.Google Scholar
Bahns, T., Beckmann, G., Gebhardt, N. and Krause, D. (2015), “Sustainability of modular product families”, In 20th International Conference on Engineering Design, ICED15, Milan, Italy, July pp. 27–30.Google Scholar
Blei, D. M., Ng, A. Y. and Jordan, M. I. (2003), “Latent dirichlet allocation”, Journal of machine Learning research, Vol. 3 No. Jan, pp. 993–1022.Google Scholar
Bolloju, N., Schneider, C. and Sugumaran, V. (2012), “A knowledge-based system for improving the consistency between object models and use case narratives”, Expert Systems with Applications, Vol. 39 No. 10, pp. 9398–9410.Google Scholar
Bonaccorsi, A. and Fantoni, G. (2007, August), “Expanding the functional ontology in conceptual design”, In International Conference on Engineering Design.Google Scholar
Cao, J., Xia, T., Li, J., Zhang, Y. and Tang, S. (2009), “A density-based method for adaptive LDA model selection”, Neurocomputing, Vol. 72 No. 7-9, pp. 1775–1781.Google Scholar
Chiarello, F., Cimino, A., Fantoni, G. and Dell'Orletta, F. (2018a), “Automatic users extraction from patents”, World Patent Information, Vol. 54, pp. 28–38.Google Scholar
Chiarello, F., Fantoni, G. and Bonaccorsi, A. (2017), “Product description in terms of advantages and drawbacks: Exploiting patent information in novel ways”, In DS 87-6 Proceedings of the 21st International Conference on Engineering Design (ICED 17) Vol 6: Design Information and Knowledge, Vancouver, Canada, 21-25.08. 2017 pp. 101–110.Google Scholar
Chiarello, F., Trivelli, L., Bonaccorsi, A. and Fantoni, G. (2018b), “Extracting and mapping industry 4.0 technologies using Wikipedia”, Computers in Industry, Vol. 100, pp. 244–257.Google Scholar
Chiu, I. and Shu, L. H. (2005, January), “Bridging cross-domain terminology for biomimetic design”, In ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, (pp. 93–101). American Society of Mechanical Engineers.Google Scholar
Chuang, J., Manning, C. D. and Heer, J. (2012, May), “Termite: Visualization techniques for assessing textual topic models”, In Proceedings of the international working conference on advanced visual interfaces, (pp. 74–77). ACM.Google Scholar
Dassisti, M., Chiarello, F., Fantoni, G., Priarone, P. C., Ingarao, G., Campana, G. and Forcelles, A. (2018), “Benchmarking the sustainable manufacturing paradigm via automatic analysis and clustering of scientific literature: A perspective from Italian technologists”, In 16th Global Conference on Sustainable Manufacturing, (pp. 1–7).Google Scholar
Deveaud, R., SanJuan, E. and Bellot, P. (2014), “Accurate and effective latent concept modeling for ad hoc information retrieval”, Document numérique, Vol. 17 No. 1, pp. 61–84.Google Scholar
Devyatkin, D., Nechaeva, E., Suvorov, R. and Tikhomirov, I. (2018), “Mapping the Research Landscape of Agricultural Sciences”, Форсайт, Vol. 12 No. 1. (eng).Google Scholar
Goonetillake, J. S., Carnduff, T. W. and Gray, W. A. (2002), “An integrity constraint management framework in engineering design”, Computers in Industry, Vol. 48 No. 1, pp. 29–44.Google Scholar
Griffiths, T. L. and Steyvers, M. (2004), “Finding scientific topics”, Proceedings of the National academy of Sciences, Vol. 101 No. 1, pp. 5228–5235.Google Scholar
Jin, J., Liu, Y., Ji, P. and Liu, H. (2016), “Understanding big consumer opinion data for market-driven product design”, International Journal of Production Research, Vol. 54 No. 10, pp. 3019–3041.Google Scholar
Lau, J. H., Grieser, K., Newman, D. and Baldwin, T. (2011, June), “Automatic labelling of topic models”, In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Vol. 1, pp. 1536–1545. Association for Computational Linguistics.Google Scholar
Trivelli, L., Apicella, A., Chiarello, F., Rana, R., Fantoni, G. and Tarabella, A. (2019), “From Precision Agriculture to Industry 4.0: unveiling technological connections in the agrifood sector”, British Food Journal, http://doi.org/10.1108/BFJ-11-2018-0747Google Scholar
Li, Z. and Tate, D. (2010), “Patent Analysis for Systematic Innovation: Automatic Function In-terpretation and Automatic Classification of Level of Invention using Natural Language Processing and Artificial Neural Networks”, International Journal of Systematic Innovation, Vol. 1 No. 2.Google Scholar
Ng, K. W., Tian, G. L. and Tang, M. L. (2011), Dirichlet and related distributions: Theory, methods and applications, Vol. 888. John Wiley & Sons.Google Scholar
Noh, H., Jo, Y. and Lee, S. (2015), “Keyword selection and processing strategy for applying text mining to patent analysis”, Expert Systems with Applications, Vol. 42 No. 9, 4348–4360.Google Scholar
Nuzzo, A., Mulas, F., Gabetta, M., Arbustini, E., Zupan, B., Larizza, C. and Bellazzi, R. (2010), “Text Mining approaches for automated literature knowledge extraction and representation”, In MedInfo pp. 954–958.Google Scholar
Parraguez, P. and Maier, A. (2017), “Data-driven engineering design research: opportunities using open data”, In DS 87-7 Proceedings of the 21st International Conference on Engineering Design (ICED 17), Vol. 7, pp. 21–25.Google Scholar
Ponweiser, M. (2012), “Latent Dirichlet allocation in R”.Google Scholar
Bailey, R. (2009), “Educating engineers for multiscale systems design in a global economy: The Technology Leaders program, ASEE Annual Conference and Exposition”, Conference Proceedings, pp. 2153–5965Google Scholar
Rehurek, R. and Sojka, P. (2010), “Software framework for topic modelling with large corpora”, In Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks.Google Scholar
Steyvers, M. and Griffiths, T. (2007), “Probabilistic topic models”, Handbook of latent semantic analysis, Vol. 427 No. 7, pp. 424–440.Google Scholar
Tseng, Y. H., Lin, C. J. and Lin, Y. I. (2007), “Text mining techniques for patent analysis”, Information Processing & Management, Vol. 43 No. 5, pp. 1216–1247.10.1016/j.ipm.2006.11.011Google Scholar
Wallach, H. M., Murray, I., Salakhutdinov, R. and Mimno, D. (2009, June), “Evaluation methods for topic models”, In Proceedings of the 26th annual international conference on machine learning, (pp. 1105–1112). ACM.Google Scholar
Wang, X. and McCallum, A. (2006, August). “Topics over time: a non-Markov continuous-time model of topical trends”, In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, (pp. 424–433). ACM.Google Scholar
Wickham, H. (2016). ggplot2: elegant graphics for data analysis. Springer.Google Scholar