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A Natural Language Approach to Biomimetic Design

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Biologically Inspired Design

Abstract

Identifying relevant analogies from biology is a significant challenge for both designers who are interested in applying biological analogies for design and researchers who are developing general methods to support biomimetic design. This chapter discusses how a natural language approach facilitates the identification of biological analogies. We review methods developed to locate useful biological knowledge in natural language format, for example books and papers, as well as apply the identified knowledge to design solutions. We then discuss how these methods can bridge the gap between the increasing amounts of available information in biology and the goal of capturing computational design knowledge for biomimetic design. Finally, application examples of the natural language approach demonstrate that this approach can identify nonobvious analogies that are based on the transfer of abstract strategies, in addition to the direct mimicries that are used in most other biologically inspired design solutions.

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Authors and Affiliations

  1. Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Canada

    L. H. Shu & Hyunmin Cheong

Authors
  1. L. H. Shu
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  2. Hyunmin Cheong
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Correspondence to L. H. Shu .

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Editors and Affiliations

  1. Georgia Institute of Technology, Atlanta, 30332, USA

    Ashok K Goel

  2. , Department of Mechanical Engineering, Texas A&M University, 411 Mechanical Engineering Office,3123 TAMU, College Station, 77843-3123, USA

    Daniel A McAdams

  3. , School of Mechanical, Industrial, and, Oregon State University, 204 Rogers Hall, Corvallis, 97331, USA

    Robert B. Stone

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Shu, L.H., Cheong, H. (2014). A Natural Language Approach to Biomimetic Design. In: Goel, A., McAdams, D., Stone, R. (eds) Biologically Inspired Design. Springer, London. https://doi.org/10.1007/978-1-4471-5248-4_3

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  • DOI: https://doi.org/10.1007/978-1-4471-5248-4_3

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-5247-7

  • Online ISBN: 978-1-4471-5248-4

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