The COVID-19 pandemic has sparked unprecedented mobilization of scientists, already generating thousands of new papers that join a litany of previous biomedical work in related areas. This deluge of information makes it hard for researchers to keep track of their own research area, let alone explore new directions. Standard search engines are designed primarily for targeted search and are not geared for discovery or making connections that are not obvious from reading individual papers . In this paper, we present our ongoing work on SciSight, a novel framework for exploratory search of COVID-19 research . Based on formative interviews with scientists and a review of existing tools, we build and integrate two key capabilities: first, exploring interactions between biomedical facets  (e.g., proteins, genes, drugs, diseases, patient characteristics); and second, discovering groups of researchers and how they are connected. We extract entities using a language model pre-trained on several biomedical information extraction tasks, and enrich them with data from the Microsoft Academic Graph (MAG). To find research groups automatically, we use hierarchical clustering with overlap to allow authors, as they do, to belong to multiple groups. Finally, we introduce a novel presentation of these groups based on both topical and social affinities, allowing users to drill down from groups to papers to associations between entities, and update query suggestions on the fly with the goal of facilitating exploratory navigation . SciSight has thus far served over 10K users with over 30K page views and 13 \% returning users. Preliminary user interviews with biomedical researchers suggest that SciSight complements current approaches and helps find new and relevant knowledge.%DIFDELCMD < \end{abstract} %DIFDELCMD < %%% \\%DIF > returns.
The COVID-19 pandemic has sparked unprecedented mobilization of scientists, generating a deluge of papers that makes it hard for researchers to keep track and explore new directions. Search engines are designed for targeted queries, not for discovery of connections across a corpus . In this paper, we present SciSight, a system for exploratory search of COVID-19 research integrating two key capabilities: first, exploring associations between biomedical facets automatically extracted from papers (e.g., genes, drugs, diseases, patient outcomes); second, combining textual and network information to search and visualize groups of researchers and their ties . SciSight has so far served over 15K users with over 42K page views and 13 %DIFDELCMD < \end{abstract} %DIFDELCMD < %%% \\%DIF > returns.
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\% returning users. Preliminary user interviews with biomedical researchers suggest that SciSight complements current approaches and helps find new and relevant knowledge.