[The book is available on Amazon and the O’Reilly digital platform.]
Related readings:
- Ontological Prisms for beginners
- Ontological Prisms & The Geometry of Knowledge
- Enterprise’s New Brains
- Knowledge Engineering with Ontological Prisms (KEOPS)
- KEOPS Kernel
- Engineering Workflows with Ontological Prisms
Contrary to naive understandings of ontologies, epistemic distinctions bring very concrete benefits, which can be summarily described as separation of concerns. As a corollary, there must be mechanisms ensuring a coherent integration of the different concerns without impairing autonomous developments. That is usually achieved through layers of indirection: semantic (thesauri), operational (taxonomies), and organizational (domains).

Semantic Indirection Layer
In flat 2D bounded contexts, words are used to characterise observed facts. As long as conversations are carried out locally and immediately, meanings can be set unambiguously; not so when they straddle contexts, which may differ for both facts and vocabularies. Nevertheless, assuming 2D semantic spaces, straddling meanings can still be aligned through unambiguous mapping of controlled vocabularies.
Indirection layers become necessary when meanings escape the confines of 2D semantic spaces, introducing non-deterministic mappings. Such a third dimension, adding conceptual spaces to operational ones, induces a mapping complexity that must be made explicit.
Operational indirection Layer
Intelligence is the ability to draw distinctions with regard to things as well as their use; while some labelled distinctions may be intrinsic, and therefore established once and for all, most are set by contexts and concerns. Hence the benefit of an indirection layer in addressing the ways things are labelled and classified.
Taking for granted that distinctions may vary across contexts and over time, the role of an indirection layer is twofold: maintain the distinction between the classification of natural (observed) facts and the classification of managed (designed) ones; ensure a continuous and consistent mapping of changes in nature (natural or managed), labels, and classifications.
Organizational indirection Layer
The emergence of systems of autonomous digital agents, replacing organizations of human agents with bounded remits, has put governance issues front and center. As a balancing act between objectives, stakes, and expectations, governance has to weigh the scope and time-frame of decisions against the visibility of business environments. Assuming that stakes and horizons can be aligned with the organization, decisions can be made by mapping problem to solution spaces, and governance can be achieved by mapping business concerns (conceptual domains) to supporting systems (managed categories). However, when stakes straddle domains, or horizons cross time frames, decisions must detour through intermediate spaces where commitments are made regarding intents, assumptions, or modus operandi; the role of the indirection layers is to manage the induced complexity.
On that account, the organizational indirection layer has to address the respective responsibilities for business (concepts) and engineering (categories) domains; the setting of tactical and strategic horizons; and the risk management policies.
Further Readings
Kaleidoscope Series
- EA Symbolic Twins
- EA Engineering interfaces
- Ontologies Use cases
- Use Cases Revisited
- Generative & General Artificial Intelligence
- Thesauruses, Taxonomies, Ontologies
- Complexity
- Cognitive Capabilities
- LLMs & the matter of transparency
- LLMs & the matter of regulations
- Learning
- Uncertainty & The Folds of Time
Other Caminao References
- Caminao Framework Overview
- Knowledge Management Booklet
- Knowledgeable Organizations
- Knowledge interoperability
- Edges of Knowledge
- ABC of EA: Agile, Brainy, Competitive
- The Pagoda Playbook
- Ontologies & Models
- Conceptual Models & Abstraction Scales
- Models & Meta-models
- Ontologies & Enterprise Architecture
- Abstraction Based Systems Engineering
- Knowledge-driven Decision-making (1)
- Knowledge-driven Decision-making (2)
- Ontological Text Analysis: Example
