Most research on semantic integrity has taken place in the traditional database fields, specifically the relational data model. Advanced models, such as semantic and object-oriented data models, have developed higher level abstractions to increase their expressive power in order to meet the needs of newly emerging application domains. This allows them to incorporate some semantic constraints directly into their schemas. There are, however, many types of restrictions that cannot be expressed solely by these high-level constructs. Therefore we extend the potential of advanced models by augmenting their abstractions with useful set restrictions. In particular, we identify and formulate four of their most common semantic groupings: set groupings, is-a related set groupings, power set groupings, and Cartesian product groupings. For each, we define a number of restrictions that control its structure and composition. We exploit the notion of object identity for the definition of these semantic restrictions. This permits each grouping to capture more subtle distinctions of the concepts in the application environment, as demonstrated by numerous examples throughout this paper. The resulting set of restrictions forms a general framework for integrity constraint management in advanced data models.>

Three of the most common fundamental groupings that are utilized in semantic database models are considered: set groupings, power set groupings, and Cartesian aggregation groupings. For each, useful restrictions that control its structure and composition are defined. This permits each grouping to capture more subtle distinctions of the concepts or situations in the application environment. The resulting set of restrictions forms a framework for integrity constraints in semantic data models. This framework is targeted towards advanced applications, such as computer-aided design, office automation, and artificial intelligence, which require the support of more sophisticated relationships among data than traditional database domains.>