A method to automatically push keywords for biological information searching in bio-inspired design

Abstract

In bio-inspired design, identifying keywords is an important step of biological information searching. Based on existing information retrieval approaches, the amount of relevant biological information in a search result largely depends on the identified keywords. Due to the limitation of biological knowledge, design engineers are difficult to identify the appropriate keywords that can find the biological information related to engineering requirements. To address this issue, we present an algorithm that can calculate the Composite Correlation Intension of functionally combined words, which integrates semantic similarity computation, data normalization, and corpus technology. Based on the algorithm, a method that automatically pushes keywords for biological information searching in bio-inspired design is also proposed. The method decomposes engineering requirements and structures functionally combined words, calculates the Composite Correlation Intension values of all functionally combined words, ranks the functionally combined words by the Composite Correlation Intension values, takes the functionally combined words with larger Composite Correlation Intension values as keywords, and pushes them. Through these keywords, design engineers retrieve the relevant biological information and discover the required design knowledge. In order to facilitate the use of proposed method, an auxiliary tool was developed in Python environment. Finally, the possibility of proposed method was demonstrated by a preliminary validation and an application case. The results show that the proposed method would be a promising alternative to identify keywords for biological information searching in bio-inspired design.

Keywords

Bio-inspired design engineering design information retrieval correlation intension auxiliary tool

Get full access to this article

View all access options for this article.

References

Cheong

Shu

. Retrieving causally related functions from natural-language text for biomimetic design. J Mech Des 2014; 136: 81008.

Brubaker

Garewal

Steinhardt

, et al. Bio-inspired counter-current multiplier for enrichment of solutes. Nat Commun 2018; 9: 1–8.

Roche

Horvath

Wamala

, et al. Soft robotic sleeve supports heart function. Sci Transl Med 2017; 9: f3925.

Triantafyllou

Weymouth

Miao

. Biomimetic survival hydrodynamics and flow sensing. Annu Rev Fluid Mech 2016; 48: 1–24.

Whittlesey

Dabiri

. Optimal vortex formation in a self-propelled vehicle. J Fluid Mech 2013; 737: 78–104.

Zhang

Aggidis

. Nature rules hidden in the biomimetic wave energy converters. Renew Sustain Energy Rev 2018; 97: 28–37.

Bar-Cohen

. Biomimetics – using nature to inspire human innovation. Bioinspir Biomim 2006; 1: 1–12.

Badarnah

Kadri

. A methodology for the generation of biomimetic design concepts. Archit Sci Rev 2015; 58: 120–133.

Nagel

JKS

Nagel

Stone

, et al. Function-based, biologically inspired concept generation. AI EDAM 2010; 24: 521–535.

10.

Shahid

Zhao

Godefroit

. Design of flying robots inspired by the evolution of avian flight. Proc IMechE Part C: J Mechanical Engineering Science 2019; 233: 7669–7686.

11.

Gao

Sun

Chen

, et al. Structural bionic design for a machine tool column based on leaf veins. Proc IMechE Part C: J Mechanical Engineering Science 2018; 232: 2764–2773.

12.

Chiu

Shu

. Biomimetic design through natural language analysis to facilitate cross-domain information retrieval. AI EDAM 2007; 21: 45–59.

13.

Kim

Lee

. A study on metadata structure and recommenders of biological systems to support bio-inspired design. Eng Appl Artif Intel 2017; 57: 16–41.

14.

Tsenn J, Linsey JS and McAdams DA. Bioinspired materials design: an assessment of methods to improve a text mining algorithm for identifying biological material structural design principles. In: IDETC/CIE, Charlotte, NC, USA, 21–24 August 2016, pp.1–10. New York: ASME.

15.

Noh

Lee

. Keyword selection and processing strategy for applying text mining to patent analysis. Expert Syst Appl 2015; 42: 4348–4360.

16.

Costantini

Crespi

Curci

. A keyword selection method for mapping technological knowledge in specific sectors through patent data: the case of biofuels sector. Econ Innov New Technol 2015; 24: 282–308.

17.

Lenau T, Dentel A, Ingvarsdóttir Þ, et al. Engineering design of an adaptive leg prosthesis using biological principles. In: International design conference, DESIGN 2010, Dubrovnik, Croatia, 17–20 May 2010, pp.331–340, https://www.designsociety.org/publication/29378/ENGINEERING+DESIGN+OF+AN+ADAP TIVE+LEG+PROSTHESIS+USING+BIOLOGICAL +PRINCIPLES.

18.

Chiu I and Shu LH. Natural language analysis for biomimetic design. In: IDETC/CIE, Salt Lake City, UT, USA, 28 September–2 October 2004, pp.1–9. New York: ASME.

19.

Miller

. WordNet: a lexical database for English. Commun Acm 1995; 38: 39–41.

20.

Shu LH and Cheong H. A natural language approach to biomimetic design. In: Goel AK, McAdams DA and Stone RB (eds) Biologically inspired design. London: Springer, 2014, pp.29–61.

21.

Schuler KK. VerbNet: a broad-coverage, comprehensive verb lexicon. PhD Thesis, University of Pennsylvania, USA, 2005.

22.

Lindemann U and Gramann J. Engineering design using biological principles. In: International design conference, Dubrovnik, Croatia, 18–21 May 2004, pp.355–360, https://www.designsociety.org/publication/19775/ENGINEERING+DESIGN+USING+BIOLOGIC AL+PRINCIPLES.

23.

Nagel J, Stone RB and Mcadams DA. An engineering-to-biology thesaurus for engineering design. In: ASME IDETC/CIE, Montreal, Canada, 15–18 August 2010, pp.1–11. New York: ASME.

24.

Glier MW, McAdams DA and Linsey JS. An experimental investigation of analogy formation using the engineering-to-biology thesaurus. In: ASME IDETC/CIE, Portland, OR, USA, 4–7 August 2013, pp.1–10. New York: ASME.

25.

Becattini

Cascini

Rosa

. Embedding biological knowledge in a conceptual design tool. Comput Aided Des Appl 2016; 13: 701–711.

26.

Hooker

Smith

. AskNature and the biomimicry taxonomy. Insight 2016; 19: 46–49.

27.

Shu

Ueda

Chiu

, et al. Biologically inspired design. CIRP Annals 2011; 60: 673–693.

28.

Mizoguchi

Kitamura

Borgo

. A unifying definition for artifact and biological functions. Appl Ontol 2016; 11: 129–154.

29.

Zeng

. Aims and Scope of JIDPS: coping with the challenges in transdisciplinary design. J Integr Des Process Sci 2015; 19: 1–3.

30.

Pahl G and Beitz W. Engineering design: a systematic approach. New York: Springer Science & Business Media, 2013.

31.

Mak

Shu

. Abstraction of biological analogies for design. CIRP Ann Manuf Technol 2004; 53: 117–120.

32.

Goel

Rugaber

Vattam

. Structure, behavior, and function of complex systems: the structure, behavior, and function modeling language. AI EDAM 2009; 23: 23–35.

33.

Bird S, Klein E and Loper E. Natural language processing with Python: analyzing text with the natural language toolkit. Sebastopol: O'Reilly Media, 2009.

34.

Jain

Shukla

Wadhvani

. Dynamic selection of normalization techniques using data complexity measures. Expert Syst Appl 2018; 106: 252–262.

35.

Lawrence E. Henderson's dictionary of biology. 14th ed. Longman Scientific & Technical: Harlow, 2008.

36.

Smith

Datta

Smith

. Oxford dictionary of biochemistry and molecular biology, Oxford: Oxford University Press, 2006.

37.

Loper E and Bird S. NLTK: the natural language toolkit. In: Proceedings of the COLING/ACL on interactive presentation sessions, Philadelphia, USA, 7 July 2002, pp.63–70. Stroudsburg: Association for Computational Linguistics.

38.

Hirtz

Stone

McAdams

, et al. A functional basis for engineering design: reconciling and evolving previous efforts. Res Eng Des 2002; 13: 65–82.

39.

Glier MW, Tsenn J, Linsey JS, et al. Methods for supporting bioinspired design. In: International mechanical engineering congress and exposition, Denver, CO, USA, 11–17 November 2011, pp.1–8. New York: ASME.

40.

Deldin J and Schuknecht M. The AskNature database: enabling solutions in biomimetic design. In: Goel AK, McAdams DA and Stone RB (eds) Biologically inspired design. London: Springer, 2014, pp.17–27.

41.

Vandevenne

Verhaegen

Dewulf

, et al. A scalable approach for ideation in biologically inspired design. AI EDAM 2015; 29: 19–31.

42.

Vandevenne

Pieters

Duflou

. Enhancing novelty with knowledge-based support for biologically-inspired design. Design Stud 2016; 46: 152–173.

43.

Leggett Dugosh

Paulus

. Cognitive and social comparison processes in brainstorming. J Exp Soc Psychol 2005; 41: 313–320.

44.

Quinn

Gaughran

. Bionics – an inspiration for intelligent manufacturing and engineering. Robot Comput Int Manuf 2010; 26: 616–621.

45.

Sachsenmeier

. Industry 5.0. – the relevance and implications of bionics and synthetic biology. Engineering 2016; 2: 225–229.

46.

Tan

Liu

Cao

, et al. Creative design inspired by biological knowledge: technologies and methods. Front Mech Eng 2019; 14: 1–14.

47.

Spiliopoulou E, Rugaber S, Goel A, et al. Intelligent search for biologically inspired design. In: International conference on intelligent user interfaces companion, Atlanta, GA, USA, 29 March–1 April 2015, pp.77–80. New York: ACM.