A term-based and citation network-based search system for COVID-19

Area of Science:

• Biomedical Informatics
• Information Science
• Epidemiology

Background:

• The COVID-19 pandemic generated an exponential increase in scientific publications across diverse disciplines.
• Navigating this massive volume of literature presents a significant challenge for researchers and domain experts.
• Existing search tools may not adequately support the exploration and discovery of complex scientific information.

Purpose of the Study:

• To develop and evaluate an exploratory search system designed to facilitate navigation of the COVID-19 scientific literature.
• To accelerate the identification of relevant documents through automated term extraction and citation analysis.
• To provide an interactive interface for exploring the scientific landscape of the pandemic.

Main Methods:

• Development of a search system integrating unsupervised term extraction and citation analysis.
• Implementation of a multi-view interactive search and navigation interface with graph visualization.
• User evaluation involving domain experts such as epidemiologists, biochemists, medicinal chemists, and medical students.

Main Results:

• Users reported satisfaction with the relevance and speed of search results.
• Participants found the system effective for exploring the scientific literature space.
• Graph visualization and filtering capabilities were highlighted as key features for discovery.

Conclusions:

• The developed exploratory search system effectively aids in navigating and discovering relevant information within the COVID-19 scientific literature.
• The integration of automated term extraction, citation analysis, and interactive visualization enhances user experience and research efficiency.
• The system's capacity for exploration and discovery was positively received by domain experts, indicating its utility in scientific research.