To take advantage of their immersion into digital environments enterprises have to differentiate between data (environment’s facts), information (systems’ representations), and knowledge (enterprise behavior).
That cannot be achieved without ironing out the semantic discrepancies between corresponding representations.
Along with the Symbolic System modeling paradigm, the aim of computer systems is to manage the symbolic representations of business objects and processes pertaining to enterprises contexts and concerns. That view can be summarized in terms of maps and territories:
Behind the various labels and modus operandi, maps can be defined on three basic layers:
- Conceptual models, targeting enterprises organization and business independently of supporting systems.
- Logical models, targeting the symbolic objects managed by supporting systems as surrogates of business objects and activities.
- Physical models, targeting the actual implementation of symbolic surrogates as binary objects.
These maps can be aligned with commonly agreed enterprise architecture layers, respectively for organizations and processes, systems functionalities, and platforms, with a fourth added for analytical models of business environments.
Ideally, that alignment should pave the way to the integration of systems and knowledge architectures, as represented by the Pagoda blueprint:
Insofar as systems engineering is concerned, that would require two kinds of transformations: from conceptual to logical models (aka analysis), and from logical to physical models (aka design).
While the latter is just a matter of expertise (thank to the GoF), that’s not the case for the former which has to deal with a semantic gap between descriptions of specific and changing business domains and organizations on one side, generic and stable systems architectures on the other side.
As a result, what can be termed a conceptual debt has accumulated with the the number of logical models supporting physical ones without the backing of relevant ones for business or organization. The objective is therefore to bring all models into a broader knowledge architecture.
Models & Ontologies
As introduced by Greek philosophers, ontologies are systematic accounts of whatever is known about a domain of concern. From that point, three basic observations can be made:
- Ontologies are made of categories of things, beings, or phenomena; as such they may range from lexicon or simple catalogs to philosophical doctrines.
- Ontologies are driven by cognitive (i.e non empirical) purposes, namely the validity and consistency of symbolic representations.
- Ontologies are meant to be directed at specific domains of concerns, whatever their epistemic nature: engineering, business, politics, religions, mythologies, astrology, etc.
With regard to models, only the second observation puts ontologies apart: compared to models, ontologies are about understanding and are not necessarily driven by empirical purposes.
On that account ontologies appear as an option of choice for the integration of symbolic representations:
- Data: instances identified at territory level, associated with terms or labels; they are mapped to business intelligence (environments) and operational (systems) models.
- Information: categories associated with sets of instances; categories can be used for requirements analysis or software design.
- Knowledge: ideas or concepts connect changing and overlapping sets of terms and categories; documents can be associated to any kind of item.
With models consistently mapped to ontologies, the conceptual debt could be restructured in the broader context of enterprise knowledge architecture.
Ontologies & Knowledge
As expounded by Davis, Shrobe, and Szolovits in their pivotal article, knowledge is made of five constituents:
- Surrogates, used as symbolic counterparts of actual objects and phenomena.
- Ontological commitments defining the categories of things that may exist in the domain under consideration.
- Fragmentary theory of intelligent reasoning defining what things can do or can be done with.
- Medium making knowledge understandable by computers.
- Medium making knowledge understandable by humans.
Points 1 and 5 are not concerned by the conceptual gap, the former being dealt with through the anchoring of identified individuals to surrogates (see below), and the latter being with human interfaces. That leaves points 2-4 as the conceptual hub where information models have to be integrated into knowledge architecture.
Assuming RDF (Resource Description Framework) graphs are used for knowledge representation (point 4), and taking a restaurant for example, the contents of information models (point 2) will be denoted by:
- Primary nodes (rectangles), for elements specific to cooking and customers relationship management, to be decorated with features (bottom right).
- Connection nodes (circles and arrows), for semantically neutral (aka syntactic) associations to be uniformly implemented across domains, e.g with predicate calculus (bottom left).
- Semantic connectors supporting both syntactic and semantic associations (bottom, middle).
Using ontologies to integrate models into knowledge architecture is to enable the restructuring of the conceptual debt.
Minding Semantic Gaps
Keeping with the financial metaphor, conceptual debts can be expressed in terms of spreads between models, and as such could be restructured through models transformation.
To begin with, all representations have to be anchored to environments through identified (#) instances.
Then, instances are to be associated to categories according to features
(properties or relationships) :
- Customers, reservations, tables, and waiters are identified individuals managed through symbolic surrogates.
- Names of dishes and ingredients do not refer to symbolic surrogates representing business objects, but are just labels pointing to recipes (documents).
- Idem for the names of wines, except for exceptional vintages with identified bottles to be managed through symbolic surrogates.
As defined above, these models can be equivalently expressed as ontologies:
- Properties are single-valued attributes.
- Relationships define links between categories.
- Aspects are structured sets of features meant to be valued through category instances.
- Documents are contents to be accessed directly or through networks, (e.g preparations or wine reviews).
It must be noted that the distinction between neutral and specific contents is not meant to be universal but be justified by pragmatic concerns, for instance:
- Addresses are not defined as aspects but as category instances so that surrogates of actual addresses can be used to optimize deliveries.
- Links to customers and addresses, being self-explanatory, can be defined as non specific.
- The relationship from dishes to ingredients is structured and specific.
Sorting out truth-preserving constructs from domain specific ones is a key success factor for models transformation, and consequently debt restructuring.
Restructuring The Debts
Restructuring financial debts means redefining assets and incomes; with regard to systems it would mean reassessing architectures with regard to value chains.
To begin with, the Pagoda blueprint central pillar is to support the integration of systems and knowledge architectures and consequently the dynamic alignment of systems capabilities, meant to be stable and shared, with business opportunities, by nature changing and specific.
Then, the pairing of systems and knowledge architectures, like a DNA double helix, is to be used to restructure both technical and conceptual debts.
With regard to technical debts, restructuring isn’t to present significant difficulties:
- Pairing income flows (applications) to tangible assets (platforms) can be done at data level.
- Model transformations between data (code) and information (models) levels can be achieved using homogeneous domain specific and programming languages.
Things are more complex with conceptual debts, for pairing as well as transformations:
- There is no direct pairing because value chains (processes) are set across assets (organization).
- Model transformations are to bridge the semantic gap between the
symbolic representations of environments (knowledge) and systems (information) .
Nonetheless, these difficulties can be overcame combining integrated architectures and ontologies.
Regarding the structure of the conceptual debt, the income part is to be defined through business objectives (customers, products, channels, supply chain, etc.), and assets to be defined by corresponding enterprise architectures capabilities.
Regarding models transformations, ontologies will be used to mind the semantic gap between environments (knowledge) and systems (information) representations:
- Power-types: describe instances of categories (age, income, education, …).
- Specialization and generalization: defined with regard of intent, subsets for individuals (wine, gender), sub-types for aspects (temperature, serve in menu).
- Knowledge based relationships (dashed line): used to describe objects and phenomena, actual, planned, or expected (face recognition of customers, influence of weather on dishes, association of wines and dishes, …
- Concepts: introduced to relate information and knowledge: gourmet.
With the backbones of symbolic representations soundly anchored to environment, it would be possible to complement functional and logical models with their conceptual counterpart and by doing so to eliminate conceptual debts. A symmetric policy could be applied to refactoring in order to redeem the technical debt associated to legacy code.
Managing Conceptual Debt
Like financial ones, conceptual debts are facts of life that have to be managed on a continuous basis. That can be achieved using Open Concepts, which may also leverage the restructuring of technical debts (TD) and consequently further in-depth digital transformation.
Ontologies would then be used to ensure:
- A separate management of models directly tied to systems, and ontologies with broader justification.
- A distinction between a kernel (aka knowledge engine), environment profiles, and business domains.
- Systems, Information, Knowledge
- Knowledge Architecture
- EA and the Pagoda Architecture Blueprint
- EA: Entropy Antidote
- Open Concepts
- Models & Meta-models
- Ontologies & Models
- Open Ontologies: From Silos to Architectures
- Ontologies & Enterprise Architecture,
- Enterprise Governance & Knowledge
- Data Mining & Requirements Analysis
- Ontologies as Productive Assets
- Caminao Ontological Kernel (Protégé/OWL 2)
- Abstractions & Emerging Architectures
- Models Transformation
- Knowledge Based Models Transformation
- The Cases for Reuse
- The Economics of Reuse
- Legacy & Modernization