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Ontological Assumptions in Information Modeling

Ontological Assumptions in Information Modeling

John M. Artz
Copyright: © 2005 |Pages: 5
ISBN13: 9781591405603|ISBN10: 1591405602|EISBN13: 9781591407959
DOI: 10.4018/978-1-59140-560-3.ch073
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MLA

Artz, John M. "Ontological Assumptions in Information Modeling." Encyclopedia of Database Technologies and Applications, edited by Laura C. Rivero, et al., IGI Global, 2005, pp. 433-437. https://doi.org/10.4018/978-1-59140-560-3.ch073

APA

Artz, J. M. (2005). Ontological Assumptions in Information Modeling. In L. Rivero, J. Doorn, & V. Ferraggine (Eds.), Encyclopedia of Database Technologies and Applications (pp. 433-437). IGI Global. https://doi.org/10.4018/978-1-59140-560-3.ch073

Chicago

Artz, John M. "Ontological Assumptions in Information Modeling." In Encyclopedia of Database Technologies and Applications, edited by Laura C. Rivero, Jorge Horacio Doorn, and Viviana E. Ferraggine, 433-437. Hershey, PA: IGI Global, 2005. https://doi.org/10.4018/978-1-59140-560-3.ch073

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Abstract

Information modeling is a technique by which a database designer develops a conceptual model of a database depicting the entity classes that will be represented in the database. There are three competing ontological assumptions that guide the modeling process. The broadest characterization of these assumptions is realism vs. conceptualism, with social realism occupying a middle ground. The realist believes that object classes exist in the real world, waiting to be discovered. The conceptualist believes that object classes are constructed in the mind of the modeler, based on observations about the application domain and the objectives of the information model. The social realist believes that classes exist as shared meanings among stakeholders in an application domain. This article explores these assumptions and then reviews selected literature in information modeling to determine which assumptions are held by key authors. It concludes that most authors hold inconsistent views, and this inconsistency provides some important insights into information modeling while presenting serious problems for practitioners and students of information modeling.

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