  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.
