Purpose

Little attention has been paid to restructuring existing massive amounts of literature data such that evidence-based meaningful inferences and networks be drawn therefrom. This paper aims to structure extant literature data into a network and demonstrate by graph visualization and manipulation tool “Gephi” how to obtain an evidence-based literature review.

Design/methodology/approach

The main objective of this paper is to propose a methodology to structure existing literature data into a network. This network is examined through certain graph theory metrics to uncover evidence-based research insights arising from existing huge amounts of literature data. From the list metrics, this study considers degree centrality, closeness centrality and betweenness centrality to comprehend the information available in the literature pool.

Findings

There is a significant amount of literature on any given research problem. Approaching this massive volume of literature data to find an appropriate research problem is a complicated process. The proposed methodology and metrics enable the extraction of appropriate and relevant information from huge quantities of literature data. The methodology is validated by three different scenarios of review questions, and results are reported.

Research limitations/implications

The proposed methodology comprises of more manual hours to structure literature data.

Practical implications

This paper enables researchers in any domain to systematically extract and visualize meaningful and evidence-based insights from existing literature.

Originality/value

The procedure for converting literature data into a network representation is not documented in the existing literature. The paper lays down the procedure to structure literature data into a network.

中文翻译：

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一种结构化文献网络荟萃分析的方法

目的

很少有人关注重组现有的大量文献数据，以便从中得出基于证据的有意义的推论和网络。本文旨在将现有文献数据构建成一个网络，并通过图形可视化和操作工具“Gephi”演示如何获得基于证据的文献综述。

设计/方法/方法

本文的主要目的是提出一种将现有文献数据结构化为网络的方法。该网络通过某些图论指标进行检查，以发现从现有大量文献数据中产生的基于证据的研究见解。从列表指标中，本研究考虑了度中心性、接近中心性和中介中心性，以理解文献库中可用的信息。

发现

关于任何给定的研究问题都有大量的文献。处理大量文献数据以找到合适的研究问题是一个复杂的过程。所提出的方法和指标能够从大量文献数据中提取适当和相关的信息。该方法通过三种不同的复习问题场景进行验证，并报告结果。

研究限制/影响

建议的方法包括更多的人工时间来构建文献数据。

实际影响

本文使任何领域的研究人员都能从现有文献中系统地提取和可视化有意义和基于证据的见解。

原创性/价值

现有文献中没有记录将文献数据转换为网络表示的过程。该论文阐述了将文献数据结构化为网络的过程。