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Automatic Extraction of Causally Related Functions From Natural-Language Text for Biomimetic Design

[+] Author Affiliations
Hyunmin Cheong, L. H. Shu

University of Toronto, Toronto, ON, Canada

Paper No. DETC2012-70732, pp. 373-382; 10 pages
doi:10.1115/DETC2012-70732
From:
  • ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 7: 9th International Conference on Design Education; 24th International Conference on Design Theory and Methodology
  • Chicago, Illinois, USA, August 12–15, 2012
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-4506-6
  • Copyright © 2012 by ASME

abstract

Identifying relevant analogies from biology is a significant challenge in biomimetic design. Our natural-language approach addresses this challenge by developing techniques to search biological information in natural-language format, such as books or papers. This paper presents the application of natural-language processing techniques, such as part-of-speech tags, typed-dependency parsing, and syntactic patterns, to automatically extract and categorize causally related functions from text with biological information. Causally related functions, which specify how one action is enabled by another action, are considered important for both knowledge representation used to model biological information and analogical transfer of biological information performed by designers. An extraction algorithm was developed and scored F-measures of 0.78–0.85 in an initial development test. Because this research approach uses inexpensive and domain-independent techniques, the extraction algorithm has the potential to automatically identify patterns of causally related functions from a large amount of text that contains either biological or design information.

Copyright © 2012 by ASME
Topics: Design , Biomimetics

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