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Representing instances: the case for reengineering conceptual modelling grammars

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  • Roman Lukyanenko
  • Jeffrey Parsons
  • Binny M. Samuel

Abstract

While many conceptual modelling grammars have been developed since the 1970s, they share the general assumption of representation by abstraction; that is, representing generalised knowledge about the similarities among phenomena in a domain (classes) rather than about domain objects (instances). This assumption largely ignores the fundamental role that instances play in the constitution of reality and in human psychology. In this paper, we argue there is a need for a grammar that explicitly recognises the primary role of instances. We examine the limitations of traditional class-based approaches to conceptual modelling, especially for modern information environments. We then explore theoretical and practical motivations for instance-based modelling, and show how such an approach can address the limitations of traditional modelling approaches. We conclude by calling for the engineering of instance-based grammars as an important direction for conceptual modelling research to address the limitations of traditional approaches, and articulate five challenges to overcome in such efforts.

Suggested Citation

  • Roman Lukyanenko & Jeffrey Parsons & Binny M. Samuel, 2019. "Representing instances: the case for reengineering conceptual modelling grammars," European Journal of Information Systems, Taylor & Francis Journals, vol. 28(1), pages 68-90, January.
    • Handle: RePEc:taf:tjisxx:v:28:y:2019:i:1:p:68-90
    DOI: 10.1080/0960085X.2018.1488567
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    Cited by:

    1. Regina Lenart-Gansiniec & Wojciech Czakon & Łukasz Sułkowski & Jasna Pocek, 2023. "Understanding crowdsourcing in science," Review of Managerial Science, Springer, vol. 17(8), pages 2797-2830, November.

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