Augmenting Scientific Creativity with an Analogical Search Engine

Hyeonsu B. Kang, Xin Qian, Tom Hope, Dafna Shahaf, Joel Chan, Aniket Kittur

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

Analogies have been central to creative problem-solving throughout the history of science and technology. As the number of scientific articles continues to increase exponentially, there is a growing opportunity for finding diverse solutions to existing problems. However, realizing this potential requires the development of a means for searching through a large corpus that goes beyond surface matches and simple keywords. Here we contribute the first end-to-end system for analogical search on scientific articles and evaluate its effectiveness with scientists' own problems. Using a human-in-the-loop AI system as a probe we find that our system facilitates creative ideation, and that ideation success is mediated by an intermediate level of matching on the problem abstraction (i.e., high versus low). We also demonstrate a fully automated AI search engine that achieves a similar accuracy with the human-in-the-loop system. We conclude with design implications for enabling automated analogical inspiration engines to accelerate scientific innovation.

Original languageAmerican English
Article number57
Pages (from-to)57:1-57:36
Number of pages36
JournalACM Transactions on Computer-Human Interaction
Volume29
Issue number6
DOIs
StatePublished - 16 Nov 2022

Bibliographical note

Publisher Copyright:
© 2022 Copyright held by the owner/author(s).

Keywords

  • Computational analogies
  • innovation
  • interactive analogical search engine
  • scientist users
  • sequence-to-sequence modeling
  • think-aloud studies
  • word embeddings

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