Scientometric analysis and knowledge mapping of literature-based discovery (1986-2020)

TL;DR

This paper provides a comprehensive scientometric overview of literature-based discovery (LBD) research from 1986 to 2020, highlighting its evolution, collaboration patterns, and emerging trends across disciplines.

Contribution

It offers the first detailed scientometric analysis of LBD, mapping its development phases, research focus shifts, and interdisciplinary integration over four decades.

Findings

LBD research has grown steadily, following Price's law.

Shift from co-occurrence to semantic-based methods in LBD.

Emerging use of deep learning and explainable AI in LBD.

Abstract

Literature-based discovery (LBD) aims to discover valuable latent relationships between disparate sets of literatures. This paper presents the first inclusive scientometric overview of LBD research. We utilize a comprehensive scientometric approach incorporating CiteSpace to systematically analyze the literature on LBD from the last four decades (1986-2020). After manual cleaning, we have retrieved a total of 409 documents from six bibliographic databases and two preprint servers. The 35 years' history of LBD could be partitioned into three phases according to the published papers per year: incubation (1986-2003), developing (2004-2008), and mature phase (2009-2020). The annual production of publications follows Price's law. The co-authorship network exhibits many subnetworks, indicating that LBD research is composed of many small and medium-sized groups with little collaboration among them. Science mapping reveals that mainstream research in LBD has shifted from baseline co-occurrence approaches to semantic-based methods at the beginning of the new millennium. In the last decade, we can observe the leaning of LBD towards modern network science ideas. In an applied sense, the LBD is increasingly used in predicting adverse drug reactions and drug repurposing. Besides theoretical considerations, the researchers have put a lot of effort into the development of Web-based LBD applications. Nowadays, LBD is becoming increasingly interdisciplinary and involves methods from information science, scientometrics, and machine learning. Unfortunately, LBD is mainly limited to the biomedical domain. The cascading citation expansion announces deep learning and explainable artificial intelligence as emerging topics in LBD. The results indicate that LBD is still growing and evolving.