  Analogies have been central to creative problem-solving throughout the
history of science and technology. As the number of scientific papers 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 papers 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.
