English

SciSight: Combining faceted navigation and research group detection for COVID-19 exploratory scientific search

Information Retrieval 2020-09-22 v3 Digital Libraries Human-Computer Interaction Machine Learning

Abstract

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 15K15K users with over 42K42K page views and 13%13\% returns.

Keywords

Cite

@article{arxiv.2005.12668,
  title  = {SciSight: Combining faceted navigation and research group detection for COVID-19 exploratory scientific search},
  author = {Tom Hope and Jason Portenoy and Kishore Vasan and Jonathan Borchardt and Eric Horvitz and Daniel S. Weld and Marti A. Hearst and Jevin West},
  journal= {arXiv preprint arXiv:2005.12668},
  year   = {2020}
}

Comments

Accepted to EMNLP 2020