The semantic data model is a way to structure data so that it’s represented in a certain logical manner. This is a conceptual data model that features semantic information, adding a meaning to the data and the relationships between them.
This process of data modeling and organization enables developers to create application programs more easily, and allows for the simple maintenance of data consistency when it’s updated.
How does the Semantic Data Model Work?
This fairly new approach revolves around semantic principles that result in a data set with structures that are inherently specified. Singular data, or a word, typically won’t convey a particular meaning to a human. But it can be given more meaning when paired with a context.
Within a database environment, data context is frequently defined by its structure (e.g. its properties, its relationships with other objects). When taking a relational approach, the data’s vertical structure is defined by explicitly referential constraints.
But with semantic modeling, this structure becomes defined in an inherent way. That means a property of the data could coincide with a reference to a different object.
We may use an abstraction hierarchy diagram to illustrate a semantic data model in a graphical format. Data types are presented as boxes, while lines represent their relationships.
This is done hierarchically, to ensure that types referencing other types remain listed above those types they reference. This makes it all simpler to read and grasp.
Semantic data model abstractions:
- Classification – “instance_of” relations
- Aggregation – “has_a” relations
- Generalization – “is_a” relations