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。科学和技术的许多重大突破都是由类比推动的：观察水使希腊哲学家克吕西普斯推测声音是一种波现象;将自行车类比使莱特兄弟设计出可操纵的飞机。随着从科学论文到专利到整个网络的在线思想库的增加，寻找类比突破的机会正在爆炸。这个项目将使用计算工具，以促进搜索从一个领域得出的类比，以帮助在另一个领域的创新思维和推理。其目标是大大加快跨领域的创新和发现，如科学，人文，数学，法律和设计。计划中的研究弥合了大规模文本挖掘方法（擅长检测表面相似性）与人类认知深度（目前在检测深层类比相似性方面无与伦比）之间的差距。研究人员将探讨如何表示与较弱的结构是强大的语言复杂性，层次结构的目的，以及抽象层次的问题，是目前在现实世界中的文件，如研究论文和研发文件。使用这些表示，研究人员将建立计算工具，使用户能够连接在一个领域的问题与解决方案从另一个领域的基础上深层结构。结果和算法可以刺激专注于深层结构的新型机器学习技术的发展，并可能对创造力，问题解决和创新领域的理论做出贡献。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值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.