Accelerating Cross-Disciplinary Innovation with Computational Analogy
Abstract
Analogy --- the ability to find and apply patterns from other domains --- is fundamental to innovation. 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. Today, the opportunities for finding analogies are exploding with the increased availability of online repositories of ideas ranging from scientific papers (Google Scholar) toproduct ideas (U.S. patent database) to the entire web. Yet, the cognitive effort and time required to fully mine the scale and diversity of these online data far exceed humans limited cognitive and temporal resources. Conversely, computational systems can scale to large amounts of data but are limited to a shallow understanding of structure and semantics, which poses a seriouschallenge to finding analogies that go beyond surface features and word-based matches. This research will develop interactive search engines that enable scientists and inventors to discover and adapt analogies from very large unstructured text datasets. The intuition behind our approach is that rather than trying to solve the problem of fully structured analogical reasoning, we instead explore the idea that for retrieving practically useful analogies, we can use weaker structural representations that can be learned and reasoned with at scale (in other words, there is a tradeoff between the ease of extraction of a structure and its expressivity). Specifically, we investigate the weaker structural representation of an ideas purpose and mechanism as a way to find useful analogies. We also explore extensions of this representation to deal with issues of hierarchies of purposes and mechanisms, and levels of abstraction that are present in real-world documents like research papers and R&D documents, as well as interaction techniques for reducing the cognitive overhead of transferring insights across domains. Using these representations and interaction techniques, we will build computational tools enabling users to connect problems in one field with solutions from another field based on their deep structure. The proposed research aims to bridge 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. Our 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. The development of analogical search engines that retrieve content based on deep structure rather than surface features could also have transformative practical impacts in a variety of fields. Instead of relying on the small number of incidental analogies found by people today, computational tools that scale up analogy finding to large data could dramatically accelerateinnovation and discovery. Augmenting human ability to rapidly integrate knowledge across a wide range of data sources and knowledge domains may also have significant benefits for the Navy and any organization that needs to address complex problems without ready-made solutions, especially if the problems require integrating many diverse perspectives. To increaseimpact, we will transfer our research results to partner organizations including Web of Science, as well as to local research groups, companies and startups.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jul 20, 2020
- Source ID
- N000142012506
Entities
People
- Joel Chan
Organizations
- Office of Naval Research
- United States Navy
- University of Maryland