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

  • Filippo Chiarello (a1), Nicola Melluso (a1), Andrea Bonaccorsi (a1) and Gualtiero Fantoni (a1)

Abstract

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.

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Copyright

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.

Corresponding author

Contact: Chiarello, Filippo, Università di Pisa, Italy, filippochiarello.90@gmail.com

References

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Bonaccorsi, A., Apreda, R. and Fantoni, G. (2017), “Cognitive and Motivational Biases in Technology Foresight”, Technological Forecasting and Social Change (Under review).
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. 391402), Springer, Berlin, Heidelberg.
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. 708722.
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. 2730.
Blei, D. M., Ng, A. Y. and Jordan, M. I. (2003), “Latent dirichlet allocation”, Journal of machine Learning research, Vol. 3 No. Jan, pp. 9931022.
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. 93989410.
Bonaccorsi, A. and Fantoni, G. (2007, August), “Expanding the functional ontology in conceptual design”, In International Conference on Engineering Design.
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. 17751781.
Chiarello, F., Cimino, A., Fantoni, G. and Dell'Orletta, F. (2018a), “Automatic users extraction from patents”, World Patent Information, Vol. 54, pp. 2838.
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. 101110.
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. 244257.
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. 93101). American Society of Mechanical Engineers.
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. 7477). ACM.
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. 17).
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. 6184.
Devyatkin, D., Nechaeva, E., Suvorov, R. and Tikhomirov, I. (2018), “Mapping the Research Landscape of Agricultural Sciences”, Форсайт, Vol. 12 No. 1. (eng).
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. 2944.
Griffiths, T. L. and Steyvers, M. (2004), “Finding scientific topics”, Proceedings of the National academy of Sciences, Vol. 101 No. 1, pp. 52285235.
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. 30193041.
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. 15361545. Association for Computational Linguistics.
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-11-2018-0747
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.
Ng, K. W., Tian, G. L. and Tang, M. L. (2011), Dirichlet and related distributions: Theory, methods and applications, Vol. 888. John Wiley & Sons.
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, 43484360.
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. 954958.
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. 2125.
Ponweiser, M. (2012), “Latent Dirichlet allocation in R”.
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. 21535965
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.
Steyvers, M. and Griffiths, T. (2007), “Probabilistic topic models”, Handbook of latent semantic analysis, Vol. 427 No. 7, pp. 424440.
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. 12161247.
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. 11051112). ACM.
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. 424433). ACM.
Wickham, H. (2016). ggplot2: elegant graphics for data analysis. Springer.

Keywords

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

  • Filippo Chiarello (a1), Nicola Melluso (a1), Andrea Bonaccorsi (a1) and Gualtiero Fantoni (a1)

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