To gather information specific to an entity, for understanding our data, we need to leverage different resources to identify individual entities, which vary widely from each physical information store. For instance, when leveraging a relational database, entities are identified using keys (e.g., customer number). Within the various information systems, many different terms are used for attributes. Thus, the notion of ontologies in this scenario allows us to determine whether entities from different applications and databases are the same or non-crucial to fusing information.
Considering that pattern of architecture there are two basic types of ontologies, horizontal and vertical.
Horizontal ontologies are general in nature, such as space-time relationships. These are common ontologies that span multiple domains, are not applicable to any single vertical space and provide a mechanism to organize and standardize information content. We've employed this type of ontology for years in the form of object models, hierarchies, taxonomies and, in many cases, XML vocabularies.
Vertical ontologies, which also incorporate features from horizontal ontologies, are domain-specific, such as natural languages for healthcare or financial services. Vertical ontologies not only define data in terms of semantics native to a particular vertical industry, they also contain rules and formal computer languages that can perform certain types of runtime automated reasoning. This means we understand the meta data and have logic bound to the meta data as well.
The use of vertical ontologies, which extend the capabilities of horizontal applications, is where the most value exists. As we learn to define these ontologies as common frameworks for specific business requirements and define the reuse of such frameworks applicable across multiple like-domains, we also learn to apply languages and reasoning techniques. Ultimately, this provides repeatable information formats, rules and logic that, in turn, provide data integration architects with the ability to leverage existing solutions rather than form them from general-purpose middleware and application development technology.
All valuable when considering understanding data, and the use of data, within our problem domain.
I have developed a system which allows each user to create their own ontology. This is supported in a number of ways by the system.
The user can ever create "complex ontologies". Those which span multipe areas of through