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Towards Formalizing Concept Drift and Its Variants: A Case Study Using Past COSIT Proceedings (Short Paper)

Authors Meilin Shi , Krzysztof Janowicz, Zilong Liu , Kitty Currier

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Author Details

Meilin Shi
  • Department of Geography and Regional Research, University of Vienna, Austria
Krzysztof Janowicz
  • Department of Geography and Regional Research, University of Vienna, Austria
Zilong Liu
  • Department of Geography and Regional Research, University of Vienna, Austria
Kitty Currier
  • Department of Geography, University of California, Santa Barbara, CA, USA

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Meilin Shi, Krzysztof Janowicz, Zilong Liu, and Kitty Currier. Towards Formalizing Concept Drift and Its Variants: A Case Study Using Past COSIT Proceedings (Short Paper). In 16th International Conference on Spatial Information Theory (COSIT 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 315, pp. 23:1-23:8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/LIPIcs.COSIT.2024.23

Abstract

In the classic Philosophical Investigations, Ludwig Wittgenstein suggests that the meaning of words is rooted in their use in ordinary language, challenging the idea of fixed rules determining the meaning of words. Likewise, we believe that the meaning of keywords and concepts in academic papers is shaped by their usage within the articles and evolves as research progresses. For example, the terms natural hazards and natural disasters were once used interchangeably, but this is rarely the case today. When searching for archived documents, such as those related to disaster relief, choosing the appropriate keyword is crucial and requires a deeper understanding of the historical context. To improve interoperability and promote reusability from a Research Data Management (RDM) perspective, we examine the dynamic nature of concepts, providing formal definitions of concept drift and its variants. By employing a case study of past COSIT (Conference on Spatial Information Theory) proceedings to support these definitions, we argue that a quantitative formalization can help systematically detect subsequent changes and enhance the overall interpretation of concepts.

Subject Classification

ACM Subject Classification
  • Information systems → Digital libraries and archives
  • Information systems → Similarity measures
  • Computing methodologies → Information extraction
Keywords
  • Concept Drift
  • Semantic Aging
  • Research Data Management

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References

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