Practical Ontology Like Things

There are many definitions/descriptions of ‘ontology’ .  One good definition/description of an ontology is provided by the National Library of Medicine on this web page Understanding Ontologies which is: 

  • Ontology: “formal, explicit specification of a shared conceptualization.”  

There are many forms that a shared conceptualization might take.  That same article provides this continuum of representation alternatives:

I call the above continuum of representation alternatives "ontology like things".  My personal working definition/description of an ontology like thing which I believe is practical and understandable is the following:

  • An ontology like thing is a formal, explicit specification of an intended shared conceptualization in logic for an area of knowledge to achieve some specific set of goals/objectives.

The precise definition of the word ontology and which term you use to describe that representation is less important than the intent of the representation.  The way to verify a conceptualization and the specific logic of that conceptualization is via testing.

The language or physical technical format used to represent the logic of the conceptualization described/defined by the ontology like thing is a different issue.  Global standard technical formats are preferred to proprietary formats.

But the ultimate test of an ontology like thing is verification to prove that the logic of the conceptualization works as expected.

The graphic below describes the sorts of things that need to be represented and other decisions which need to be made to express a conceptualization:


The major taxonomic ranks and classifications of things described can vary. But fundamentally, an "ontology" or "ontology like thing" is a tool. 

As a tool, the limitations of the tool must be understood in order to employ the tool effectively. Common criticisms and discussions related to the notion of an ontology include the following:

  • Vagueness and ambiguity: Anything created by humans tends to not be perfect.  What exactly constitutes a “conceptualization”?  What does “explicit specification” actually mean; that might vary significantly between different contexts.
  • Scope and scalability: An ontology like thing should cover all the important necessary aspects of some area of knowledge. Trying to get an ontology like thing to do everything can be challenging.  This is why defining the goals and objectives of the intended use of an ontology like thing is important.
  • Adaptability:  Knowledge is fluid, dynamic; not static.  The world changes, ontology like things need to be adaptable to that changing world.  But ontology like things can tend to be static. Rather, an ontology like thing needs to be adaptable to dynamic environments where new information can be integrated into a system continuously.
  • Context-dependency: Context is important.  What works in one area of knowledge might not work in some other area of knowledge.  Transferability of an ontology from one intended purpose to some other intended purpose or generalizing the ontology like thing may not work.
  • Bias:  Human bias in any ontology like thing must be understood and managed.  Certain ontology like things might not be suitable or optimal for computational systems. Human bias can limit the design and functionality of ontology like things intended for use by artificial intelligence type applications.
  • Interoperability: Many times, ontology like things are meant to solve interoperability problems within systems through the creation of a shared, common understanding; reality is often more complex. Different ontology like things might interpret the same concepts in different ways, leading to difficulties in integrating systems or data that use different frameworks.

These and other criticisms and discussions will continue.  A master craftsmen must have a good understanding of the limitations of their tools.  Criticisms and discussions lead to refinement of the tools.  Use of these tools over time will push the tools towards more dynamic, interoperable, adaptable, context-aware systems.

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