Information Modeling Languages
In 1976, an entity-relationship (ER) graphic notation was introduced by Peter Chen. He stressed that it was a "semantic" modelling technique and independent of any database modelling techniques such as Hierarchical, CODASYL, Relational etc. Since then, languages for information models have continued to evolve. Some examples are the Integrated Definition Language 1 Extended (IDEF1X), the EXPRESS language and the Unified Modeling Language (UML). In the 1980s there were several approaches to extend Chen’s Entity Relationship Model. Also important in this decade is REMORA by Colette Rolland.
The ICAM Definition (IDEF) Language was developed from the U.S. Air Force ICAM Program during the 1976 to 1982 timeframe. The objective of the ICAM Program, according to Lee (1999), was to increase manufacturing productivity through the systematic application of computer technology. IDEF includes three different modeling methods: IDEF0, IDEF1, and IDEF2 for producing a functional model, an information model, and a dynamic model respectively. IDEF1X is an extended version of IDEF1. The language is in the public domain. It is a graphical representation and is designed using the ER approach and the relational theory. It is used to represent the “real world” in terms of entities, attributes, and relationships between entities. Normalization is enforced by KEY Structures and KEY Migration. The language identifies property groupings (Aggregation) to form complete entity definitions.
EXPRESS was created as ISO 10303-11 for formally specifying information requirements of product data model. It is part of a suite of standards informally known as the STandard for the Exchange of Product model data (STEP). It was first introduced in the early 1990s. The language, according to Lee (1999), is a textual representation. In addition, a graphical subset of EXPRESS called EXPRESS-G is available. EXPRESS is based on programming languages and the O-O paradigm. A number of languages have contributed to EXPRESS. In particular, Ada, Algol, C, C++, Euler, Modula-2, Pascal, PL/1, and SQL. EXPRESS consists of language elements that allow an unambiguous object definition and specification of constraints on the objects defined. It uses SCHEMA declaration to provide partitioning and it supports specification of data properties, constraints, and operations.
UML is a modeling language for specifying, visualizing, constructing, and documenting the artifacts, rather than processes, of software systems. It was conceived originally by Grady Booch, James Rumbaugh, and Ivar Jacobson. UML was approved by the Object Management Group (OMG) as a standard in 1997. The language, according to Lee (1999), is non-proprietary and is available to the public. It is a graphical representation. The language is based on the objected-oriented paradigm. UML contains notations and rules and is designed to represent data requirements in terms of O-O diagrams. UML organizes a model in a number of views that present different aspects of a system. The contents of a view are described in diagrams that are graphs with model elements. A diagram contains model elements that represent common O-O concepts such as classes, objects, messages, and relationships among these concepts.
IDEF1X, EXPRESS, and UML all can be used to create a conceptual model and, according to Lee (1999), each has its own characteristics. Although some may lead to a natural usage (e.g., implementation), one is not necessarily better than another. In practice, it may require more than one language to develop all information models when an application is complex. In fact, the modeling practice is often more important than the language chosen.
Information models can also be expressed in formalized natural languages, such as Gellish. Gellish, which has natural language variants Gellish Formal English, Gellish Formal Dutch (Gellish Formeel Nederlands), etc. is an information representation language or modeling language that is defined in the Gellish smart Dictionary-Taxonomy, which has the form of a Taxonomy/Ontology. A Gellish Database is not only suitable to store information models, but also knowledge models, requirements models and dictionaries, taxonomies and ontologies. Information models in Gellish English use Gellish Formal English expressions. For example, a geographic information model might consist of a number of Gellish Formal English expressions, such as:
whereas information requirements and knowledge can expressed for example as follows:
- towerSuch Gellish expressions use names of concepts (such as 'city') and relation types (such as
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