Analogies in distant domain often lead to scientitic discoveries. However, it can be prohibitively difficult for researchers to find useful analogies from unfamiliar domains as it is poorly supported by search engines. We introduce Solvent, a mixed-initiative system where annotators structure abstracts of acedemic papers into different aspects and use a semantic model to find analogies among research papers and across different domains. These results demonstrate a novel path towards computationally supported knowledge sharing in research communities.
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.
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