CHS: Small: Innovation Through Analogical Search

两件事之间的比较有助于支持创造性思维和推理。科学技术的许多重大突破都是由类比推动的：通过观察水，希腊哲学家克里西普斯推测声音是一种波浪现象；类比自行车，莱特兄弟设计了一种可操纵的飞机。随着从科学论文到专利再到整个网络的在线思想仓库的可用性增加，找到类比突破的机会正在爆炸式增长。这个项目将使用计算工具来促进搜索从一个领域得出的类比，以帮助在另一个领域进行创新思维和推理。其目标是大幅加快科学、人文、数学、法律和设计等不同领域的创新和发现。这项计划中的研究弥合了大规模文本挖掘方法的能力和人类认知深度之间的差距，前者擅长检测表面相似性，后者目前在检测深层类比相似性方面是无与伦比的。研究人员将探索，在研究论文和研发文件等现实世界文件中存在的语言复杂性、目的层级和抽象程度等问题上，结构较弱的表示形式是如何具有健壮性的。使用这些表示法，调查人员将构建计算工具，使用户能够基于深层结构将一个领域的问题与另一个领域的解决方案联系起来。结果和算法可能会刺激专注于深层结构的新型机器学习技术的发展，并可能在创造力、解决问题和创新领域回馈理论。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Comparisons between two things are useful to support creative thinking and reasoning. Many important breakthroughs in science and technology were driven by analogy: observing water led the Greek philosopher Chrysippus to speculate that sound was a wave phenomenon; an analogy to a bicycle allowed the Wright brothers to design a steerable aircraft. Opportunities to find analogical breakthroughs are exploding with the increased availability of online repositories of ideas ranging from scientific papers to patents to the entire web. This project will use computational tools to facilitate searching for analogies drawn from one domain to help with innovative thinking and reasoning in another domain. The goal is to dramatically accelerate innovation and discovery across domains as diverse as science, humanities, mathematics, law, and design. The planned research bridges the gap between the power of large-scale text mining approaches, which excel at detecting surface similarity, and the depth of human cognition, which is currently unsurpassed at detecting deep analogical similarity. Investigators will explore how representations with weaker structures are robust to issues of language complexity, hierarchies of purposes, and levels of abstraction that are present in real-world documents like research papers and R&D documents. Using these representations, investigators will build computational tools enabling users to connect problems in one field with solutions from another field based on deep structure. Results and algorithms could spur the development of new types of machine-learning techniques that focus on deep structure, and may contribute back to theory in the fields of creativity, problem solving, and innovation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.