See also: Knowledge Management Booklet
As far as standards go, the more they are, the less they’re worth.
What have we got
Assuming that modeling languages are meant to build abstractions, one would expect their respective ladders converging somewhere up in some conceptual or meta cloud.
Assuming that standards are meant to introduce similarities into diversity, one would expect clear-cut taxonomies to be applied to artifacts designs.
Instead one will find bounty of committees, bloated specifications, and an open-minded if clumsy language confronted to a number of specific ones.
What is missing
Given the constitutive role of mathematical logic in computing systems, its quasi absence in modeling methods of their functional behavior is dumbfounding. Formal logic, set theory, semiotics, name it, every aspect of systems modeling can rely on a well established corpus of concepts and constructs. And yet, these scientific assets may be used in labs for research purposes but they remain overlooked for any practical use; as if the laser technology had been kept out of consumers markets for almost a century.
What should be done
The current state of affairs can be illustrated by a Horse vs Zebra metaphor: the former with a long and proved track record of varied, effective and practical usages, the latter with almost nothing to its credit except its archetypal idiosyncrasy.
Like horses, logic can be harnessed or saddled to serve a wide range of purposes without loosing anything of its universality. By contrast, concurrent standards and modeling languages can be likened to zebras: they may be of some use for their owner, but from an outward perspective, what remains is their distinctive stripes.
So the way out of the conundrum seems obvious: get rid of the stripes and put back the harness of logic on all the modeling horses.
What Can Be Done
Frameworks are meant to promote consensus and establish clear and well circumscribed common ground; but the whopping range of OMG’s profiles and frameworks doesn’t argue in favor of meta-models.
Ontologies by contrast are built according to the semantics of domains and concerns. So whereas meta-models have to mix lexical, syntactic, and semantic constructs, ontologies can be built on well delineated layers.
An ontological kernel has been developed as a Proof of Concept of the benefits of ontologies for enterprise architecture, the purpose being to extend the Caminao enterprise architecture paradigm to contexts and environments. That kernel has been built on two 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.
A beta version (Protégé/OWL 2) will soon (Q1 2021) be available for comments on the Stanford/Protégé portal.
Right, but at the end of the day business expectations will have to submit to systems logical minds, so modeling languages should play their role
Bits of that corpus do make it into individual projects. Unfortunately, in my experience, many, if not most, projects face adaptation to illogical biases, competing requirements and organizational power struggles that embed those Zebra stripes on the outcome.
Tell the truth and leave quickly.
ASM