Covid-19 has been an unprecedented challenge that disruptively reshaped societies and brought a massive amount of novel knowledge to the scientific community. However, as this knowledge flood has surged, researchers have been disadvantaged by not having access to a platform that can quickly synthesize rapidly emerging information and link the expertise it contains to established knowledge foundations. Aiming to fill this gap, in this paper we propose a research framework that can assist scientists in identifying, retrieving, and understanding Covid-19 knowledge from the ocean of scholarly articles. Incorporating Principal Component Decomposition (PDC), a knowledge model based on text analytics, and hierarchical topic tree analysis, the proposed framework profiles the research landscape, retrieves topic-specific knowledge and visualizes knowledge structures. Addressing 127,971 Covid-19 research papers from PubMed, our PCD topic analysis identifies 35 research hotspots, along with their correlations and trends. The hierarchical topic tree analysis further segments the knowledge landscape of the whole dataset into clinical and public health branches at a macro level. To supplement this analysis, we also built a knowledge model from research papers on vaccinations and fetched 92,286 pre-Covid publications as the established knowledge foundation for reference. The hierarchical topic tree analysis results on the retrieved papers show multiple relevant biomedical disciplines and four future research topics: monoclonal antibody treatments, vaccinations in diabetic patients, vaccine immunity effectiveness and durability, and vaccination-related allergic sensitization. 
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                            SciSight: Combining faceted navigation and research group detection for COVID-19 exploratory scientific search
                        
                    
    
            The COVID-19 pandemic has sparked un- precedented mobilization of scientists, gener- ating 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 con- nections 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 ex- tracted from papers (e.g., genes, drugs, dis- eases, patient outcomes); second, combining textual and network information to search and visualize groups of researchers and their ties. SciSight1 has so far served over 15K users with over 42K page views and 13% returns 
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                            - Award ID(s):
- 1735194
- PAR ID:
- 10211978
- Date Published:
- Journal Name:
- Proceedings of the 2020 EMNLP (Systems Demonstrations), Association for Computational Linguistics
- Page Range / eLocation ID:
- 135-143
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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