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Summary
This summary is machine-generated.

This study introduces a graph-based search for COVID-19 literature, enabling users to explore complex research relationships visually. The approach enhances evidence discovery beyond traditional keyword searches.

Keywords:
CORD-19COVID-19 Open Research DatasetNamed Entity RecognitionNeo4jbig data corpusclinical researchco-occurrence networkgraph searchtext mining

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Information Science

Background:

  • The COVID-19 pandemic generated over a million studies, creating a vast corpus of unstructured abstracts.
  • Existing keyword-based searches for this literature are limited, lacking visual support and struggling with complex queries.

Purpose of the Study:

  • To develop a graph-based search system for COVID-19 literature to improve exploration and querying.
  • To leverage concept graphs for representing and querying scientific knowledge in a user-friendly manner.

Main Methods:

  • Annotated COVID-19 abstracts using terms from the Unified Medical Language System and Ontology of Coronavirus Infectious Disease.
  • Constructed a co-occurrence network of concepts with connections based on mutual information.
  • Developed a graph query engine supporting exact and partial matches, with query completion suggestions.

Main Results:

  • Created a co-occurrence network with 128,249 entities and 47,198,965 relationships.
  • Implemented a GRAPH-SEARCH interface for interactive network exploration and query formulation.
  • Generated ranked bibliographies with publications linked to specific query components for clarity.

Conclusions:

  • The developed approach facilitates query formulation and evidence retrieval from large text corpora.
  • This method is adaptable to other scientific domains with available document corpora and ontologies.