A systematic review of methods for aligning, mapping, merging taxonomies in information sciences
ISSN: 0022-0418
Article publication date: 4 May 2023
Issue publication date: 24 October 2023
Abstract
Purpose
The purpose of this study is to provide a systematic literature review on taxonomy alignment methods in information science to explore the common research pipeline and characteristics.
Design/methodology/approach
The authors implement a five-step systematic literature review process relating to taxonomy alignment. They take on a knowledge organization system (KOS) perspective, and specifically examining the level of KOS on “taxonomies.”
Findings
They synthesize the matching dimensions of 28 taxonomy alignment studies in terms of the taxonomy input, approach and output. In the input dimension, they develop three characteristics: tree shapes, variable names and symmetry; for approach: methodology, unit of matching, comparison type and relation type; for output: the number of merged solutions and whether original taxonomies are preserved in the solutions.
Research limitations/implications
The main research implications of this study are threefold: (1) to enhance the understanding of the characteristics of a taxonomy alignment work; (2) to provide a novel categorization of taxonomy alignment approaches into natural language processing approach, logic-based approach and heuristic-based approach; (3) to provide a methodological guideline on the must-include characteristics for future taxonomy alignment research.
Originality/value
There is no existing comprehensive review on the alignment of “taxonomies”. Further, no other mapping survey research has discussed the comparison from a KOS perspective. Using a KOS lens is critical in understanding the broader picture of what other similar systems of organizations are, and enables us to define taxonomies more precisely.
Keywords
Acknowledgements
Part of this work has been supported by the Beta Phi Mu Eugene Garfield Doctoral Dissertation Fellowship and a dissertation fellowship from the University of Illinois at Urbana-Champaign. Cheng would like to thank her doctoral committee members, Dr. Bertram Ludäscher, Dr. Allen Renear, Dr. Karen Wickett and Dr. Nico Franz, for their kind feedback on this work that is a vital component of her dissertation. The authors would also like to express their gratitude to Dr. Ly Dinh, Dr. Rhiannon Bettivia, Dr. Michael Gryk, the reviewers, and the editor for their constructive comments.
Citation
Cheng, Y.-Y. and Xia, Y. (2023), "A systematic review of methods for aligning, mapping, merging taxonomies in information sciences", Journal of Documentation, Vol. 79 No. 6, pp. 1413-1439. https://doi.org/10.1108/JD-01-2023-0003
Publisher
:Emerald Publishing Limited
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