A Category Theory Approach to Conceptual Data Modeling

This paper describes a category theory semantics for conceptual data modeling. The conceptual data modeling technique used can be seen as a generalization of most existing conceptual data modeling techniques. It contains features such as specialization, generalization , and power types. The semantics uses only simple category theory constructs such as (co)limits and epi-and monomorphisms. Therefore, the semantics can be applied to a wide range of instance categories, it is not restricted to topoi or cartesian closed categories. By choosing appropriate instance categories, features such as missing values, multi-valued relations, and uncertainty can be added to conceptual data models.