Caminao Ontological Kernel (Protégé/OWL 2)


This kernel is meant to be a Proof of Concept and a workbench for a ontological approach to enterprise architecture frameworks.

What they see is what you get (Mantegna)

For that purpose the kernel is organized along two basic principles:

  • A clear-cut distinction between truth-preserving representation and domain specific semantics.
  • Profiled ontologies designed according to the nature of contents (concepts, documents, or artifacts), layers (environment, enterprise, systems, platforms), and contexts (institutional, professional, corporate, social.

OWL 2 implementation ensures portability and interoperability.

A beta version is available for comments on the Stanford/Protégé portal with the link: Caminao Ontological Kernel (CaKe).


From a theoretical perspective the objective is to demonstrate the benefits of the Stanford modeling paradigm that defines systems as containers managing symbolic representations (aka surrogates) of operational environments.

From a functional perspective the kernel is to meet the principles of knowledge representation set by Davis, Shrobe, and Szolovits  in their seminal article:

  1. Surrogate: KR provides a symbolic counterpart of actual objects, events and relationships.
  2. Ontological commitments: a KR is a set of statements about the categories of things that may exist in the domain under consideration.
  3. Fragmentary theory of intelligent reasoning: a KR is a model of what the things can do or can be done with.
  4. Medium for efficient computation: making knowledge understandable by computers is a necessary step for any learning curve.
  5. Medium for human expression: one the KR prerequisite is to improve the communication between specific domain experts on one hand, generic knowledge managers on the other hand.

On that basis ontologies deal with numbers 1,2,3, and 5, and models with numbers 4 and 5. The proposed approach is to use ontologies for profiles and domains as to support a seamless conceptual integration with model based systems engineering (MBSE).


That is to be achieved through a compact set of unambiguous concepts, straightforward modeling principles, and implementation with Stanford University’s Protégé/OWL 2.

Things: Concepts, Categories, Aspects, Documents

The kernel is built on four kind of things (terms) corresponding to the epistemic nature of targeted items:

Cake 00


  • Concepts (e.g Contract): intensional concepts come with core necessary semantics detached of any context or purpose; extensional concepts orbit intensional ones and denote heterogeneous sets of instances.
  • Documents (e.g Specs): terms for information contents.
  • Categories (aka classes, aka types): terms for sets of instances. Power-types represent categories of categories.
  • Aspects: terms for structural or functional descriptions that cannot be instantiated (aka valued) on their own.

Besides concepts and documents, which correspond to thesaurus and content management systems (CMS), the backbone of the kernel is built around categories, aspects, and power-types.

As detailed by the Caminao modeling paradigm, core functional categories (object, agent, actor, …) serve as pillars for architecture models , their aspects being detailed using generic constructs (collection, composition, graph, logic, …) applied uniformly across categories.

Cake Hierarch

Power-types  represent categories of categories introduced when categories are to be managed as business objects on their own, possibly across domains.

Aspects are set according to standard, non specific, and truth-preserving constructs for:

  • Features (Properties and operations).
  • Numeric and logic expressions.
  • Abstract data types (collections, graphs, …)
  • Rules

Contrary to categories, whose instances are to be checked for external consistency, aspects have only to be checked for internal consistency.

In line with the objective of sorting out contents (specific) and representation (generic), specialization semantics associated with OWL’s classes and object and data properties are reset according to targets:

  • Conceptual sub-types are intensional: specialization of meaning.
  • Documents sub-types are intensional: specialization of topics.
  • Category sub-types are extensional: subsets of instances.
  • Aspect sub-types are functional: subsets of features.
  • Sub-properties for connectors: subsets of instances.
  • Sub-properties for data: subsets of features.

Rules are the nexus of ontologies and their design is to determine the whole of knowledge architecture:

  • Rules are defined by left or/and right footprints and expressions.
  • Sub-classes for rules are set with regard to their impact on the coupling between systems and contexts: deontic rules are defined externally, alethic ones are set by governance.
  • The way rules are executed (pushed or pulled) is determined by modus operandi and synchronization properties.

Object Properties

Beside predefined object properties and references to modeling contexts, three kind of connectors are distinguished, depending on targets:Cake object props

  • Semiotic connectors deal with the meaning of terms at linguistic level; they are meant to support cognitive operations independently of domain-specific semantics.
  • Syntactic connectors do the same for representations; they are meant to support truth-preserving operations independently of domain semantics.
  • Functional connectors are defined by modeling context; symbolic references, activity flows, processes synchronization, or communication channels; their semantics is meant to be neutral  with regard to domains.

Data Properties

The kernel introduces a number of data properties beside OWL 2 native ones:

Cake data props


  • Architecture level: {“enterprise” , “environment” , “platforms” , “systems”}. Used to define and manage ontology profiles.
  • Social identity: Literal.
  • Behavior: {“passive” , “proactive” , “reactive”}. Used to characterize objects and rules as well as for consistency checks.
  • Change footprint:  {“expectation” , “physical” , “state” , “symbolic”}. Used to characterize events and consistency checks.
  • Rule modus-operandi: backward or forward.
  • Statutory context: {“institutional” , “professional” , “corporate”,”social”, “personal”}.
  • Embodiment: {“hybrid” , “physical” , “statutory”}. Used to characterize the organizational basis of collective agents.
  • Exclusivity: Boolean. Used to characterize power-types.
  • Mutability: Boolean. Used to characterize power-types.
  • Partitions: Enumeration. Used to characterize power-types.
  • Extensional: Boolean. True if associated with instances. Used for consistency checks.
  • Language abilities: Boolean for formal, natural, and domain specific language ability. Used for consistency checks.
  • Synchronization: {“asynchronous” , “detached” , “synchronous”}. Used to characterize the coupling between actual and symbolic realms, and check consistency.

Annex: Examples

These examples are focused on three key benefits of the approach, namely:

  • The distinction between representation syntax and contents semantics.
  • The distinction between truth-preserving and functional reasoning.
  • The processing of data into information and knowledge.

Syntax & Semantics: Categories, Aspects, Properties

The distinction between objects and aspects has a well established track record in software design and should be fully supported by ontologies.

  • Social Id and roles are structural aspects of manager entity, i.e identified by and used through managers’ instances (a).
  • Roles is a symbolic container for multiple (b) functional connectors (c).
  • Terms for actors and roles are deemed synonymous (d)
Connectors: syntactic (a, b), functional (c), semiotic (d)

Interfaces consistency can be checked by comparing agents’ actual language capabilities to actors’ required ones.

Integrity: Structural vs Functional Constraints

Factoring out truth-preserving reasoning is a direct benefit of the distinction between representation and domain specific contents.

With mutability uniformly and unambiguously defined across domains, a distinction can be made between what is known and what is open to scrutiny.

  • The relationships between cars and models cannot be modified (dark blue).
  • The relationships between rentals and cars can be modified (clear blue).


By contrast, the cardinality of occurrences is best defined by domains as it combines functional and structural integrity constraints.

  • Existential integrity: rentals must be uniquely associated to cars and groups; but groups can mix models.
  • Functional integrity: as business may require multiple assignments of actual cars to rental ones, mandatory constraints should not be mixed with exclusivity ones.
  • Inferred connections can be directly specified with the tool.

These object and data properties may have to be refined as to limit the overlapping with OWL 2 native ones.

From Data to Knowledge: Individuals, Categories, Concepts

The benefits of the distinction between representation syntax and domain semantics, as well as its corollary for truth-preserving reasoning, encompass all information systems, and so ensure a sound basis for knowledge management.

But knowledge is dynamic by essence and what is at stake is the processing of raw data into information and knowledge assets:

  • Data is first captured through aspects.
  • Categories are used to process data into information.
  • Concepts serve as bridges to knowledgeable information.

Information models describe what systems are meant to manage in terms of categories, e.g for customers and business processes. Insofar as ontologies are concerned, individuals are logical instances, e.g invoicing insurers or customer with family.



Data is first acquired as hypothetical structures to be mapped to information models, e.g through applying statistical knowledge.

Knowledge is managed through expressions, concepts, and documents. When knowledge is considered, the critical point is to decide what should count as individuals.

Further Reading

External Links