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.