Systematic scientometric reviews, empowered by computational and visual analytic approaches, offer opportunities to improve the timeliness, accessibility, and reproducibility of studies of the literature of a field of research. On the other hand, effectively and adequately identifying the most representative body of scholarly publications as the basis of subsequent analyses remains a common bottleneck in the current practice. What can we do to reduce the risk of missing something potentially significant? How can we compare different search strategies in terms of the relevance and specificity of topical areas covered? In this study, we introduce a flexible and generic methodology based on a significant extension of the general conceptual framework of citation indexing for delineating the literature of a research field. The method, through cascading citation expansion, provides a practical connection between studies of science from local and global perspectives. We demonstrate an application of the methodology to the research of literature-based discovery (LBD) and compare five datasets constructed based on three use scenarios and corresponding retrieval strategies, namely a query-based lexical search (one dataset), forward expansions starting from a groundbreaking article of LBD (two datasets), and backward expansions starting from a recently published review article by a prominent expert in LBD (two datasets). We particularly discuss the relevance of areas captured by expansion processes with reference to the query-based scientometric visualization. The method used in this study for comparing bibliometric datasets is applicable to comparative studies of search strategies.
|Publication status||Published - 2019 Oct|
Bibliographical noteFunding Information:
CC acknowledges the support of the SciSIP Program of the National Science Foundation (Award #1633286), the support of Microsoft Azure Sponsorship. Data sourced from Dimensions, an inter-linked research information system provided by Digital Science (https://www.dimensions.ai). MS acknowledges the support of the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2018S1A3A2075114) and partial support from the Yonsei University Research Fund of 2019-22-0066. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We are grateful to Neil Smalheiser for his valuable suggestions on the paper. The work is supported by the SciSIP Program of the National Science Foundation (Award #1633286). CC acknowledges the support of Microsoft Azure Sponsorship. Data sourced from Dimensions, an inter-linked research information system provided by Digital Science (https://www.dimensions.ai). This work is also supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2018S1A3A2075114). This research is also partially supported by the Yonsei University Research Fund of 2019-22-0066.
© 2019 Chen, Song. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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