An important conceptual modeling activity in the development of database, object-oriented and agent-oriented systems is the capture and expression of domain constraints governing underlying data and object states. UML is increasingly used for capturing conceptual models, as it supports conceptual modeling of arbitrary domains, and has extensible notation allowing capture of invariant constraints both in the class diagram notation and in the separately denoted OCL syntax. However, a need exists for increased formalism in constraint capture that does not sacrifice ease of use for the analyst. In this paper, we codify a set of invariant patterns formalized for capturing a rich category of propositional constraints on class diagrams. We use tools of Boolean logic to set out the distinction between these patterns, applying them in modeling by way of example. We use graph notation to systematically uncover constraints hidden in the diagrams. We present data collected from applications across different domains, supporting the importance of "pattern-finding" for n-variable propositional constraints using general graph theoretic methods. This approach enriches UML-based conceptual modeling for greater completeness, consistency, and correctness by formalizing the syntax and semantics of these constraint patterns, which has not been done in a comprehensive manner before now

The authors present a portion of the EARL semantic data model, an augmentation of the entity-relationship model, which uses notational constructs based on propositional logic to allow a greater selection of semantic integrity constraints to be captured during the specification and design of a domain schema. The use of these logic-based constructs is shown to allow more subtle constraints to be specified, so that ambiguity can be reduced when placing constraints on the universe of discourse for database or knowledge-base applications. The notion of what are semantic constraints is discussed, and notational constructs with informal semantics are defined for the EARL model. Several examples are presented.>

An approach is described for enhancing an integrated relational data dictionary in order to capture and faithfully present this high-level enterprise schema in a form which can be incorporated directly into the relational database system. The approach, referred to as EDICT, allows for the specification of the enterprise schema for a database as an extension of the data dictionary by storing the schema as a set of normalized tables. In addition, it facilitates more effective use of existing data dictionaries by providing an extended metaschema which describes the structure and use of the dictionary itself.>