Enterprise Ontology-like Things

Ontology-like things (a.k.a. knowledge graph systems) are becoming increasingly important.  Ontology-like things are machine-readable maps that help you understand your data and turn that data into information to make use of that data.

It is critically important to get those ontology-like things right because they will drive work in the information age.  As this LinkedIn post points out "layering" is important when you construct your "enterprise ontology" and provides this graphic of what layers make up such an enterprise ontology:

There are other dynamics also.  Modularity when creating ontologies is important so that pieces can be reused effectively.  Access is also important; some ontologies will be publicly available while other ontologies will be private.

Remember; when you think of "ontology", recognize that there are many different approaches to representing "ontology-like things" in machine-readable form: (larger image)


Quality matters.  Currently, there is not really an agreed upon way of measuring the quality of an ontology-like thing.  But good, quality ontology-like things will work; bad ontology-like things will not get you where you want to go.

Ontology-like things need to be maintained and have other life-cycle considerations.

Today, there is a lot of knowledge represented in unstructured and semi-structured form that needs to be structured.  Also, while some knowledge that can be considered "structured" or "semi-structured"; that structure might be for the presentation of knowledge as contrast to the meaning of that knowledge which make using or reusing that knowledge challenging.

Some, but not all, knowledge will be transitioned from unstructured and semi-structured formats with a focus on the meaning of information as contrast to the presentation of information.


A key thing to understand is that eventually hybrid AI will prevail. As Alan Morrison points out in this article; different AI have different capabilities.  As knowledge "moves around" between unstructured, semi-structured and structured forms with a focus on presentation versus meaning; the right balance will emerge.

All this knowledge will be connected in some way into a "fabric" or "mesh" or "assembly" of knowledge.  Lean Six Sigma techniques and principles will be applied to keep quality where it needs to be.  Enterprises that leverage this fabric will potentially prevail over those that do not as new approaches to performing tasks and processes are created.  Imagine something like Engine B's "Copilot"  that sits on top of a financial reporting scheme that is connected to a properly created XBRL taxonomy. that works within an expert system for creating financial reports.  Many have talked about "algorithmic regulation" (see page 18) but few can actually do it.  Apply design patterns and logic patterns to build powerful yet safe software systems using not "silo thinking" but rather "systems thinking".

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