Semantic data models

Abstract

Semantic data models have emerged from a requirement for more expressive conceptual data models. Current generation data models lack direct support for relationships, data abstraction, inheritance, constraints, unstructured objects, and the dynamic properties of an application. Although the need for data models with richer semantics is widely recognized, no single approach has won general acceptance. This paper describes the generic properties of semantic data models and presents a representative selection of models that have been proposed since the mid-1970s. In addition to explaining the features of the individual models, guidelines are offered for the comparison of models. The paper concludes with a discussion of future directions in the area of conceptual data modeling. INTRODUCTIONAlthough the relational model has provided database practitioners with a modeling methodology independent of the details of the physical implementation, many designers believe that the relational model does not offer a sufficiently rich conceptual model for problems that do not map onto tables in a straightforward fashion. The past decade has seen the emergence of numerous data models with the aims of providing increased expressiveness to the modeler and incorporating a richer set of semantics into the database. This collection of data models can be loosely categorized as "semantic" data models since their one unifying characteristic is that they attempt to provide more semantic content than the relational model. The first research papers on semantic data models appeared approximately 7 years after Codd's initial publications describing the relational model. Thus, in perhaps another 5-7 years, one of the modeling methodologies discussed here may attain commercial viability. This survey selects a representative sampling of the new generation of data

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