A formal framework for describing and classifying semantic data models

Abstract

The current lack of a classification system for Semantic Data Models (SDMs) is the result of their dependence on standard predicate calculus, which is limited in expressive power. Consequently, many issues in Database Theory cannot adequately be expressed, such as: temporal concepts (before/after insert/update/delete), the triple: true, false, unknown “facts”, existent and non-existent “objects” (null), private and “overlapping” Databases with—potentially—conflicting data, i.e. “possible” and “shared” worlds. The classification framework presented is based on systems of logic with greater expressive power than predicate calculus. In these logics, factual, semantic or intensional, modal, and probabilistic statements can be expressed. Two general classification criteria are established: 1. (a) what can be modelled or expressed in the SDM? 2. (b) what can be derived or deduced in the SDM? The descriptive and classificatory apparatus developed provides a satisfactory framework for representing and explaining the topics above and other theoretical issues, such as “open-world assumption” vs “closed-world assumption”, “null-objects” vs “unknown objects”. The individual features are applied to the analysis and comparison of five Database Models, among them Codd's RM/T and Abrial's binary model. The adequacy of the criteria is further shown by demonstrating that the descriptive features used by Tsichritzis/Lochovsky in Data Models are contained (as a proper subset) in the framework. The paper concludes with an outlook on inductive systems.