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.

翻译：生物启发设计（BID），又称仿生学，是一种通过类比自然界解决工程难题的方法论。例如，速比涛公司工程师基于鲨鱼皮设计泳衣。为现实问题寻找相关生物解决方案面临重大挑战，这既源于工程师和设计师通常有限的生物学知识，也受限于现存的BID资源不足。现有BID数据集均为人工精选且规模较小，扩容需要高昂的人工标注成本。本文提出BARcode（生物类比检索器），一种可大规模自动从互联网挖掘生物灵感的搜索引擎。借助自然语言理解和数据编程领域的最新进展，BARcode能够识别工程挑战的潜在灵感来源。实验表明，BARcode可为应对现实问题的工程师和设计师检索有价值的灵感，并重现历史上著名的BID案例。我们已公开相关数据与代码，并视BARcode为破解历史性阻碍BID在工程创新中实际应用难题的重要进展。