Solvent: A mixed initiative system for finding analogies between research papers

Joel Chan, Joseph Chee Chang, Tom Hope, Dafna Shahaf, Aniket Kittur

Research output: Contribution to journalArticlepeer-review

50 Scopus citations


Scientific discoveries are often driven by finding analogies in distant domains, but the growing number of papers makes it difficult to find relevant ideas in a single discipline, let alone distant analogies in other domains. To provide computational support for finding analogies across domains, we introduce Solvent, a mixed-initiative system where humans annotate aspects of research papers that denote their background (the high-level problems being addressed), purpose (the specific problems being addressed), mechanism (how they achieved their purpose), and findings (what they learned/achieved), and a computational model constructs a semantic representation from these annotations that can be used to find analogies among the research papers. We demonstrate that this system finds more analogies than baseline information-retrieval approaches; that annotators and annotations can generalize beyond domain; and that the resulting analogies found are useful to experts. These results demonstrate a novel path towards computationally supported knowledge sharing in research communities.

Original languageAmerican English
Article number31
JournalProceedings of the ACM on Human-Computer Interaction
Issue numberCSCW
StatePublished - Nov 2018

Bibliographical note

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© 2018 Copyright held by the owner/author(s). Publication rights licensed to ACM.


  • Analogy
  • Computer-supported cooperative work
  • Crowdsourcing
  • Scientific discovery


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