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.
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Availability of data and materials
The data set discussed in this paper has been deposited in the public repository Zenodo (https://doi.org/10.5281/zenodo.3884422) and is freely available to the research community.
Code availability
R code to replicate the results of the study is accessible on the author’s GitHub page (https://github.com/akastrin/lbd-review).
Notes
A 2-generation forward expansion collects all papers connecting to the seed paper with two-step citation paths.
The Journal of Biomedical Discovery and Collaborations (DISCO) was an open access online journal that encompassed all aspects of scientific information management and studies of scientific practice. The journal connected disparate perspectives (e.g., informatics, computer science, sociology, cognitive psychology, scientometrics, public policy, technology innovation, and history and philosophy of science) and published several papers directly related to LBD. DISCO was published by BioMed Central from 2006–2008.
Sigma (\(\Sigma\)) index is used to characterize scientific novelty according to centrality and burstness as criteria of transformative discovery (Chen et al. 2009). Sigma is defined as \((\textit{centrality} + 1)^{\textit{burstness}}\).
To our knowledge two LBD events have been organized in the past decade. The First International Workshop on the role of Semantic Web in Literature-Based Discovery (SWLBD 2012) was co-organized with The IEEE International Conference on Bioinformatics and Biomedicine in Philadelphia, USA (http://www.ischool.drexel.edu/ieeebibm/bibm12). At the time of this writing, Smalheiser and Sebastian organized the First International Workshop on Literature-Based Discovery (LBD2020) co-located with the 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining in Singapore (http://scientificarbitrage.com/lbd-2020).
At the time of the submission of this paper we came across a new paper (Crichton et al. 2020) from Korhonen’s group which discussed implementation of graph-based neural network methodology for open and closed LBD.
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Acknowledgements
The authors thank Petra Hrovat Hristovski for proof-reading the manuscript. The authors also thank Halil Kilicoglu for his helpful comments and suggestions.
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Authors were supported by the Slovenian Research Agency (Grant Nos. Z5-9352 (AK) and J5-1780 (DH)).
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AK conceived the study, collected the data, performed data analysis, and wrote the manuscript. DH contributed with critical revisions of the manuscript. Both authors read and approved the final version of the manuscript.
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Kastrin, A., Hristovski, D. Scientometric analysis and knowledge mapping of literature-based discovery (1986–2020). Scientometrics 126, 1415–1451 (2021). https://doi.org/10.1007/s11192-020-03811-z
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DOI: https://doi.org/10.1007/s11192-020-03811-z