Defining Concept Drift and Its Variants in Research Data Management: A Scientometric Case Study on Geographic Information Science
- Author(s)
- Meilin Shi, Krzysztof Janowicz, Zilong Liu, Mina Karimi, Ivan Majic, Alexandra Fortacz
- Abstract
Just as the language used to express them, concepts constantly evolve, due to changing cultural norms, scientific progress, or major events that (re)shape our understanding of the world. For example, as humans increasingly contribute to disasters, e.g., by altering the landscape, the line between natural and anthropogenic disasters blurs. This has far-reaching consequences for disaster response, policymaking, and also research. In research data management (RDM), evolving terms like natural disaster or global warming bring challenges for data retrieval, integration, interpretation, and reuse. This highlights the importance of studying concept drift, defined as the changes in a concept’s meaning over time. While concept drift has been explored in numerous domains, including cognitive science, linguistics, semiotics, and ontology evolution, a clear definition within RDM is still lacking. This is significant as it may reduce recall during RDM-based data retrieval. To bridge this gap, this work proposes definitions of concept drift and related phenomena, such as shifts and merges, to foster cross-domain consensus. Using a scientometric dataset in GIScience, we apply these definitions to identify representative cases of concept drift. Our findings reveal that concepts in the GIScience domain may undergo various changes, including birth, drift, and shift, among others. These findings emphasize the need to monitor concept drift in RDM to uphold FAIR data principles, thereby improving precision and recall when searching for relevant literature or data. Lastly, we highlight the potential to measure concept drift over space (place) in addition to time alone.
- Organisation(s)
- Department of Geography and Regional Research
- Journal
- Transactions in GIS
- ISSN
- 1361-1682
- DOI
- https://doi.org/10.1111/tgis.70058
- Publication date
- 05-2025
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 102015 Information systems
- Portal url
- https://ucrisportal.univie.ac.at/en/publications/905873dc-8b87-4eac-af64-4e5f44e06de0