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Psychological Foundations for Concept Modeling

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Diagrammatic Representation and Inference (Diagrams 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2980))

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Abstract

A fundamental research activity in the field of Information Systems involves the proper design of data based systems. An important stage in construction is to determine the relevant, meaningful information structures in a domain, and to document these in an accurate and unambiguous way. Diagrammatic modeling notations have evolved as tools to facilitate this process. However an appropriate formal semantics to clarify the interpretation of these notations is difficult to define. This can result in models that are subjective and difficult to interpret by external parties. Recently, philosophical ontologies that provide a taxonomy of elements in the world have been proposed as a foundation to ground the symbols in diagrams. We argue that models represent a designer‘s psychological perception of the world rather than some abstract description of that world. An ontology of these perceptions is therefore more relevant for the design of diagrammatic notations used in documenting and unambiguously communicating the analysis of a domain. We present an ontology of mental concepts from cognitive science, and find support for a prediction concerning ternary relations. Importantly, an influential thesis based on a philosophy of ”real world” ontology makes the opposite prediction. We show that properties of the mind, rather than the world, should guide diagramming convention.

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References

  1. Jackendoff, R.: Semantics and Cognition. MIT Press, Cambridge (1983)

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  2. Wand, Y., Storey, V.C., Weber, R.: An Ontological Analysis of the Relationship Construct in Conceptual Modeling. ACM Transactions on Database Systems 24(4), 495–528 (1999)

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© 2004 Springer-Verlag Berlin Heidelberg

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Veres, C., Mansson, G. (2004). Psychological Foundations for Concept Modeling. In: Blackwell, A.F., Marriott, K., Shimojima, A. (eds) Diagrammatic Representation and Inference. Diagrams 2004. Lecture Notes in Computer Science(), vol 2980. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25931-2_5

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  • DOI: https://doi.org/10.1007/978-3-540-25931-2_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-21268-3

  • Online ISBN: 978-3-540-25931-2

  • eBook Packages: Springer Book Archive

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