What is it about?
Modeling can be hard and mistakes may be unwittingly made unless the modeling language/environment can automatically flag inconsistencies. Orthogonal Ontological Classification represents a desirable middle-ground between language principles that are too restrictive and those who are not able to reliably protect users from making modeling errors.
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Why is it important?
Orthogonal Ontological Classification is intuitive to use but rigorous at the same time. It allows users to cleanly separate modeling concerns and receive feedback when concerns are not internally consistent.
Perspectives
Orthogonal Ontological Classification shields users from the complexity of the notions required to ensure the consistency of models. Users do not need to learn elaborate language constructs but are nevertheless protected from unintentionally combining basic ones in a manner that does not add up to a consistent whole.
Thomas Kuehne
Victoria University of Wellington
Read the Original
This page is a summary of: Multi-dimensional multi-level modeling, Software & Systems Modeling, January 2022, Springer Science + Business Media,
DOI: 10.1007/s10270-021-00951-5.
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