Retrieving Causally Related Functions From Natural Language Text for Biomimetic Design

Abstract

Identifying biological analogies is a significant challenge in biomimetic (biologically inspired) design. This paper builds on our previous work on finding biological phenomena in naturallanguage text. Specifically, a rulebased computational technique is used to identify biological analogies that contain causal relations. Causally related functions describe how one function is enabled by another function, and support the transfer of functional structure from analogies to design solutions. The causalrelation retrieval method uses patterns of syntactic information that represent causally related functions in individual sentences, and scored Fmeasures of 0.73–0.85. In a user study, novice designers found that of the total search matches, proportionally more of the matches obtained with the causalrelation retrieval method were relevant to design problems than those obtained with a single verbkeyword search. In addition, matches obtained with the causalrelation retrieval method increased the likelihood of using functional association to develop design concepts. Finally, the causalrelation retrieval method enables automatic extraction of biological analogies at the sentence level from a large amount of naturallanguage sources, which could support other approaches to biologically inspired design that require the identification of interesting biological phenomena.