Information model

An information model in software engineering is a representation of concepts and the relationships, constraints, rules, and operations to specify data semantics for a chosen domain of discourse.

It can provide sharable, stable, and organized structure of information requirements or knowledge for the domain context.

Typically, they are used to model a constrained domain that can be described by a closed set of entity types, properties, relationships and operations.

[1] Research by contemporaries of Peter Chen such as J.R.Abrial (1974) and G.M Nijssen (1976) led to today's Fact Oriented Modeling (FOM) languages which are based on linguistic propositions rather than on "entities".

[4] The objective of the ICAM Program, according to Lee (1999), was to increase manufacturing productivity through the systematic application of computer technology.

The language identifies property groupings (Aggregation) to form complete entity definitions.

[1] EXPRESS was created as ISO 10303-11 for formally specifying information requirements of product data model.

It uses SCHEMA declaration to provide partitioning and it supports specification of data properties, constraints, and operations.

[1] UML is a modeling language for specifying, visualizing, constructing, and documenting the artifacts, rather than processes, of software systems.

UML contains notations and rules and is designed to represent data requirements in terms of O-O diagrams.

In practice, it may require more than one language to develop all information models when an application is complex.

For example, a geographic information model might consist of a number of Gellish Formal English expressions, such as: whereas information requirements and knowledge can be expressed for example as follows: Such Gellish expressions use names of concepts (such as 'city') and relation types (such as ⟨is located in⟩ and ⟨is classified as a⟩) that should be selected from the Gellish Formal English Dictionary-Taxonomy (or of your own domain dictionary).

The Gellish English Dictionary-Taxonomy enables the creation of semantically rich information models, because the dictionary contains definitions of more than 40000 concepts, including more than 600 standard relation types.