In conventional data modeling usually various attribute types that provide information about the same kind of thing are grouped into entity types (or ‘object types’). As a consequence of that higher order relations (tables) are created. However, many of those higher order relations must be changed when business requirements change. Thus most conventional data models have structures that are business requirements specific.
Semantic models have a universal structure that is independent of business requirements. This is achieved by expressing all requirements by binary relations, including also higher order relations. This even increases the semantic content of the expressions, as is explained and illustrated in ‘Semantic Modeling in Formal English’.
Some people have the incorrect idea that an expression of a binary relation by definition consists of two things only. However, the expression of a binary relation consists of a (usually pretty large) collection of components, such as an identifier of the relation, the (two) related things, the kind of relation, the intention of the expression, its status and timing, validity context, possibly cardinality constraints, and various other contextual facts. The advantage of those other components is that contextual facts can be provided for every binary relation.
Forthermore, modeling with binary relations has a number of other advantages, such as:
Conventional ‘entities’ with their ‘attributes’ are usually higher order relations, because they relate more than two 'attributes'. Some of such relations appear to be natural higher order relations, whereas others appear to be artificial higher order relations. Higher order relations can also be represented by equivalent collections of binary relations, while explicitly including the higher order relations as elements in the model and nevertheless using only binary relation to express their semantics. Thus without loosing any meaning but even by increasing the semantic content. That is done in Semantic Modeling in the following way.
In conventional methodologies, modeling of change over time is often difficult or even ‘not supported’. Semantic modeling provides easy modeling of change over time. This is caused by the following difference: when the life of a relation in a conventional model is terminated (the relation is deleted) the consequence is that the entity (and the attributes) do not exist anymore. However, when the life of a relation in a semantic model is terminated, then the related things remain existent. As a consequence, in a semantic model every binary relation can be terminated and replaced by another binary relation as and when required, exept for binary classification and specialization relations. The modeling of change over time might include replacing one component by another as is modeled by binary relations as follows: