The purpose of the Systems Ontology is to provide a framework, in the form of an ontology, for capturing system details resulting from systems analysis.

The Systems Ontology is composed of three distinct parts:

• SystemThing holds the core concepts of a system – not to be changed
• SystemSpace is a definition of categories applicable to different concepts describing specific systems – should change rarely as the understanding of the different types of systems evolves
• System is the place for the instances of specific systems – may change regularly as the analysis and understanding of specific systems progresses.

The following diagram is a representation of the different elements in each part.

Core concepts and Spaces
Core concepts define the fundamental building blocks for the Systems Ontology in a set of abstract classes of mainly two types: Thing and Space.
Things describe the different aspects of a system on very high abstraction level (meta-meta in this case).
Spaces provide further refinement for the different aspects of a system.

Structure
The structure of the system is described in abstract sub-classes of StructuredThing. The structure defines the relationships between concepts, it defines what form (in terms of graphs) of construct can be built. Constructs can be for example: chain, ring, tree, net, a combination of these or any other formation.

Specific structures are defined inheriting from the StructuredThing abstract class, making StructuredThing a collection, a container of various types.

An example structure: ComposableThing has two slots, has_part and is_part_of (both are each other’s inverse), since has_part is a multi-value, is_part_of a single-value slot referring to instances, the structure for ComposableThing is going to be a tree.

Category
Categories are defined in the CategorySpace, which holds a hierarchy of categories underneath. CathegoryThing enables instances of a system elements to reference multiple categories from the CategorySpace.

CategorySpace is a bit unusual as far as modelling categories goes. Usually there is a meta-model for describing the characteristics of a category on the meta-model level then each category is an instance in the model often in some form of relationship with other categories.
In this case, a different, perhaps unusual approach is taken when categories are defined as classes and sub-classes of classes. This will allows to build a hierarchy of categories and use the class name for the name of the category, which should be sufficient as no attributes are necessary.
There is one more twist, instead of assigning a class (category) as a superclass to the categorized class, the CategorizedThing defines a slot with the value type of a class, in other words the category is defined as an attribute (slot).

This approach allows to define the categories in the meta-model in a hierarchy and still use them as an attribute in the model.

Life-cycle
The notation of life-cycle makes the concepts “alive”, in other words, it represents the time factor.

Individual life-cycles are captured in the LifeCycleSpace as sub-classes. Life-cycles consist of states, these are captured as instances of a life-cycle class in the ontology. LiveThing enables the system instances to reference individual states and other related instances – behaviour and quality (details in the next section).

Note that life-cycles are not described as proper state-machines in this ontology. There is no notation of events or state transitions other than registering the preceding and following states for each state instance.

Behavior and Quality
The following assumption was made: Only “living” things (ie concepts with life-cycle) show characteristics of behavior and qualities.
Based on this assumption, behaviour and quality details can be registered to any LivingThing.

Behavior and Quality are two concepts represented in the ontology in a similar fashion.
BehaviorSpace consists of sub-classes, where sub-classes are considered as categories of behavior. These categories can serve as a mechanism to group, qualify, arrange the long list of different behaviors a system may have.
Specific behaviors are captured as instances of a Class from the BehaviorSpace.

Qualities are represented the same way, where categories are sub-classes of the QualitySpace and specific qualities are instances of a class from the QualitySpace.

System instances
The root for capturing specific system instances is SystemSpace, it inherits a set of abstract classes (LivingThing and CategorizedThing) describing a system.

Besides creating instances for system elements a few other types of instances will be created along the way including: life-cycle states, behaviors and qualities.

States
Life-cycles consist of states, individual state instances should be created under the relevant life-cycle categories.
States are later referenced from system instances, where a system can only have one state at a time in the current setup of the ontology.

Behaviours and Qualities
Behaviours and Qualities are instances of simple classes in the pertinent categories. In the current ontology each instance is represented with a name (slot) only.
The categories should be carefully chosen for both set of concepts, otherwise can be time consuming to re-factor an instance of a quality into a category in order to register finer grain qualities. Same challenge applies to registering behavior.

Topology and Systems
Systems, and sub-systems, are captured in some form of hierarchy in the ontology – the Topology. The Topology for systems is not pre-defined as it mostly depends on the method applied to systems analysis, and somewhat depends on the different types of systems as well.
Topology is withing the System ontology, it is captured together with the system instances, unlike the categories for life-cycle, behavior and quality.

System instances are created within the topology, where they automatically inherit characteristics of LivingThing and CategorizedThing.
The topology does not have a notation of structure for system elements, therefore predefined (in the SystemSpace) structures should be applied as Super-classes to specific topology classes. Note that sub-classes in the topology will inherit structures from parent classes, for that reason structures should be used sparingly in classes closer to the root and should be applied to classes closer to the instances.

Data characteristics of system elements are defined within of the Topology by adding them to specific classes. Data details are esentially attributes (slots here) on a class. One should be careful making sure that data is not confused with structure. Slots with references to other system instances are perfectly valid data elements, however these can be easily confused with structure elements also represented as slots. The success of proper separation will depend on the rigour applied to systems analysis.

A good definition for Ontology (in computer science) is available from Wikipedia.

An ontology … is a formal representation of a set of concepts within a domain and the relationships between those concepts. It is used to reason about the properties of that domain, and may be used to define the domain.

The tool of choice

Protégé Ontology Editor and Knowledge Acquisition System has its root in Medical Informatics, it was made to capture medical information in a structured manner to form a basis for medical knowledge that can be processed, searched, queried later.

My intent of using an ontology was quite similar, but I targetted a different domain: Enterprise Architecture.

A few pointers that supported the idea:

• Using ontology to support development of software architectures
• Enterprise Architecture Model and Repository
• Federal Enterprise Architecture Reference Model Ontology (FEA-RMO)
• Building Internet Distributed Computing Applications, Knowledge mining at the enterprise level

There are articles also available on the Web arguing the use of ontologies and Protege as a tool for supporting Enterprise Architecture.

More links for research the use of ontology:

• Ontology Building: A Survey of Editing Tools
• Ontology Tools Survey, Revisited
• An older (2002) comparison table for Ontology editors

OK, it is Protege, now which version?

Protege offers two versions – V3 and V4 – of the tool, where the new major version is significantly different from the previous. The Protege Web site also offers a side-by-side comparison helping with the decision.

My take on deciding which version to use:

• OWL is not made for human consumption and at this point in the tool’s life too much OWL is exposed in the interface
• No Frames support means no interface to populate the meta-model (ontology) with instances – another reminder that OWL is not designed for human consumption
• V4 is beta at this moment, while V3 is a mature ironed out version
• There is already a tool to help migrating Frames to OWL, once V4 has the functionality and stability of V3 then moving to the new version should be relatively straight-forward
• If you do not need OWL, just use Frames – V3!

There is more coming on how to use Protege to support Enterprise Architectures.