A Search Engine for Discovery of Scientific Challenges and Directions

Authors

  • Dan Lahav Tel Aviv University
  • Jon Saad Falcon Allen Institute for Artificial Intelligence Georgia Institute of Technology
  • Bailey Kuehl Allen Institute for Artificial Intelligence
  • Sophie Johnson Allen Institute for Artificial Intelligence
  • Sravanthi Parasa Swedish Medical Center
  • Noam Shomron Tel Aviv University
  • Duen Horng Chau Georgia Institute of Technology
  • Diyi Yang Georgia Institute of Technology
  • Eric Horvitz Microsoft
  • Daniel S. Weld Allen Institute for Artificial Intelligence University of Washington
  • Tom Hope Allen Institute for Artificial Intelligence University of Washington

DOI:

https://doi.org/10.1609/aaai.v36i11.21456

Keywords:

AI For Social Impact (AISI Track Papers Only)

Abstract

Keeping track of scientific challenges, advances and emerging directions is a fundamental part of research. However, researchers face a flood of papers that hinders discovery of important knowledge. In biomedicine, this directly impacts human lives. To address this problem, we present a novel task of extraction and search of scientific challenges and directions, to facilitate rapid knowledge discovery. We construct and release an expert-annotated corpus of texts sampled from full-length papers, labeled with novel semantic categories that generalize across many types of challenges and directions. We focus on a large corpus of interdisciplinary work relating to the COVID-19 pandemic, ranging from biomedicine to areas such as AI and economics. We apply a model trained on our data to identify challenges and directions across the corpus and build a dedicated search engine. In experiments with 19 researchers and clinicians using our system, we outperform a popular scientific search engine in assisting knowledge discovery. Finally, we show that models trained on our resource generalize to the wider biomedical domain and to AI papers, highlighting its broad utility. We make our data, model and search engine publicly available.

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Published

2022-06-28

How to Cite

Lahav, D., Saad Falcon, J., Kuehl, B., Johnson, S., Parasa, S., Shomron, N., Chau, D. H., Yang, D., Horvitz, E., Weld, D. S., & Hope, T. (2022). A Search Engine for Discovery of Scientific Challenges and Directions. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 11982-11990. https://doi.org/10.1609/aaai.v36i11.21456