Digitized Knowledge-Based Literature Reviewing: A Tutorial on Coding Causal and Process Models as Graphs

11 Pages Posted: 30 Jun 2021 Last revised: 9 Jan 2023

Date Written: June 20, 2021

Abstract

Current literature reviewing approaches rely on using keywords to search for relevant articles, reading them, and manually synthesizing their knowledge. This approach is inefficient and limits the pace of scientific progress. To address such issues, we have developed a graph-based approach to digitizing knowledge. The method focuses on coding the core knowledge in publications (i.e., causal or process models) as directed graphs following a well-defined regimen and combining the directed graphs into a labeled property graph database. We provide guidance on coding publications as graphs using a graph query language. An application has been developed to facilitate scholars to code publications as graphs to load into a graph database. We discuss the contributions of the method for literature reviewing.

Keywords: Knowledge digitization, Literature review, Causal model, Graph database, Graph query language

Suggested Citation

Song, Yuanyuan and Watson, Richard Thomas and Zhao, Xia, Digitized Knowledge-Based Literature Reviewing: A Tutorial on Coding Causal and Process Models as Graphs (June 20, 2021). Available at SSRN: https://ssrn.com/abstract=3870782 or http://dx.doi.org/10.2139/ssrn.3870782

Yuanyuan Song (Contact Author)

The University of Georgia

Athens, GA 30605
United States

Xia Zhao

University of Georgia ( email )

610 S. Lumpkin St.
Benson C404
Athens, GA 30602
United States

No contact information is available for Richard Thomas Watson

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