The terms Taxonomy and Ontology are frequently confused; here are some basic definitions:
“Taxonomy is the practice and science of classification…Taxonomies, or taxonomic schemes, are composed of taxonomic units known as taxa (singular taxon), or kinds of things that are arranged frequently in a hierarchical structure, typically related by subtype-supertype relationships, also called parent-child relationships. In such a subtype-supertype relationship the subtype kind of thing has by definition the same constraints as the supertype kind of thing plus one or more additional constraints…Originally the term taxonomy referred to the classifying of living organisms (now known as alpha taxonomy); however, the term is now applied in a wider, more general sense and now may refer to a classification of things, as well as to the principles underlying such a classification. Almost anything — animate objects, inanimate objects, places, concepts, events, properties, and relationships — may be classified according to some taxonomic scheme.”
Ontology─ “In both computer science and information science, an ontology is a data model that represents a set of concepts within a domain and the relationships between those concepts. It is used to reason about the objects within that domain. Ontologies are used in artificial intelligence, the Semantic Web, software engineering, biomedical informatics and information architecture as a form of knowledge representation about the world or some part of it. Ontologies generally describe:
- Individuals: the basic or “ground level” objects
- Classes: sets, collections, or types of objects
- Attributes: properties, features, characteristics, or parameters that objects can have and share
- Relations: ways that objects can be related to one another
- Events: the changing of attributes or relations”
“If you did not define attributes for the concepts you would have either a taxonomy or a controlled vocabulary. These are useful, but are not considered true ontologies.” (Wikipedia)
What is the real difference between taxonomies and ontologies?
A Taxonomy is a hierarchical representation of a specific data set…Taxonomies are useful in software for navigation, in fact Amazon.com does something like this. You select “Books”, and you can then select a subcategory, and drill down from there.
An Ontology is a set of concepts and the relationships between them. Concepts are nouns, and relationships are verbs. Two commonly used relationships are “is a“ and “has a“. Concepts have attributes, which are things that describe the concept. For instance, there is a concept “Retirement Account”. An IRA is a retirement account, and a 401(k) is a retirement account. An IRA “has a” balance. A balance is itself a concept, that has a “date” attribute and an amount attribute. In computer science, these things are relevant for areas such as search and text analysis…
That is the basic difference: ontology defines concepts and how they relate, and a taxonomy is a hierarchical breakdown of items.”
How are taxonomy and ontology used in EA?
For User-centric EA, taxonomies are useful for categorizing the elements of the enterprise into a useful and usable architecture. For example, in the business architecture, we use a taxonomy to classify our organization’s functions into core mission, mission support, and business support categories. The taxonomy adds value by providing context and depth to the information.
Additionally, ontology is necessary in enterprise architecture for identifying the relationships between elements in the architecture and for building the information repository in a relational database. For example, Systems are related to the business functions they support as well as to the underlying technologies that make up the system. The relationships between the perspectives of the architecture (performance, business, information, services, technology, security) and their elements is actually where core value from the information is derived from. Each perspective of the architecture has important information, but it is in relating the information, that deeper analysis becomes possible. For example, in a straight taxonomy of systems and applications, we may understand what systems and how many the organization has; however, when we relate those systems, for example, to functional areas, then we can see which functions have redundant systems or gaps and which are mission critical systems based on the functions they support.
The use of both taxonomy and ontology is important and necessary to structures the enterprise architecture and to analyzing it and provide meaningful findings and recommendations.