A Keyword-Based Literature Review Data Generating Algorithm—Analyzing a Field from Scientific Publications

Junchao Wang,Guodong Su,Xiwei Huang,Lingling Sun,Chengrui Wan

doi:10.3390/sym12060903

Abstract

A scientific review is a type of article that summarizes the current state of a specific field, which is crucial for promoting the advancement of our science community. Authors need to read hundreds of research articles to prepare the data and insights for a comprehensive review, which is time-consuming and labor-intensive. In this work, we present an algorithm that can automatically extract keywords from the meta-information of each article and generate the basic data for review articles. Two different fields—communication engineering, and lab on a chip technology—were analyzed as examples. We first built an article library by downloading all the articles from the target journal using a python-based crawler. Second, the rapid automatic keyword extraction algorithm was implemented on the title and abstract of each article. Finally, we classified all extracted keywords into class by calculating the Levenshtein distance between each of them. The results demonstrated its capability of not only finding out how communication engineering and lab on a chip were evolved in the past decades but also summarizing the analytical outcomes after data mining of the extracted keywords. Our algorithm is more than a useful tool for researchers during the preparation of a review article, it can also be applied to quantitatively analyze the past, present and help authors predict the future trend of a specific research field.

Highlights

With the development of advanced science and emerging technologies, more and more interdisciplinary fields have become the frontiers of scientific research, which are crucial to the welfare of all human beings
We are motivated by the question, is it possible that a non-expert researcher could summarize what a field has happened in the past few years and what the major accomplishment of the corresponding field is? is it possible for us to generate the data for the review article automatically? In this work, we present an algorithm that generate the data to be further used in a review article by leveraging automatic keyword extraction and similarity calculation
As an example of our algorithm, we summarized the major accomplishments of two different fields, communication engineering and lab on a chip, from two scientific journals, IEEE transaction on communications and Lab on a Chip, respectively

Summary

Introduction

With the development of advanced science and emerging technologies, more and more interdisciplinary fields have become the frontiers of scientific research, which are crucial to the welfare of all human beings. Artificial intelligence (AI), Internet of Things (IoT) and big data are helping radiologists to develop deep neural networks for classification, detection, and segmentation tasks of different diseases that are threatening the health of millions of patients [5,6]. Researchers and engineers from Tissue engineering [7], Genetic engineering [8], Bioinformatics [9], Biological systems engineering [10], Biotechnology [11] and other fields are developing usable, tangible or economically viable Bioengineering products such as bionic eye [12], supramolecular biomaterials [13], among others, for the benefit of all people. A computer engineer who is developing a deep-learning-based cancer classifier should at least have a basic understanding of how radiologists are detecting cancer using computer tomography (CT) [14,15]. To develop a feasible bionic eye, electronic engineers shall reach tissue engineers for help to make the bionic eye bio-compatible and minimize rejection reaction [12]

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Conclusion