  Biologically Inspired Design (BID), or Biomimicry, is a problem-solving
methodology that applies analogies from nature to solve engineering challenges.
For example, Speedo engineers designed swimsuits based on shark skin. Finding
relevant biological solutions for real-world problems poses significant
challenges, both due to the limited biological knowledge engineers and
designers typically possess and to the limited BID resources. Existing BID
datasets are hand-curated and small, and scaling them up requires costly human
annotations.
  In this paper, we introduce BARcode (Biological Analogy Retriever), a search
engine for automatically mining bio-inspirations from the web at scale. Using
advances in natural language understanding and data programming, BARcode
identifies potential inspirations for engineering challenges. Our experiments
demonstrate that BARcode can retrieve inspirations that are valuable to
engineers and designers tackling real-world problems, as well as recover famous
historical BID examples. We release data and code; we view BARcode as a step
towards addressing the challenges that have historically hindered the practical
application of BID to engineering innovation.
