Digital Financial Reporting

Michael Uschold, Senior Ontology Consultant with Semantic Arts, provides this presentation Ontologies and Semantics for Industry where he explains the benefits of ontologies and ontology-like things to industry.

Ushold points out that there is a plethora of 'ontology-like things'.  I like the term "ontology-like things". That is what the ontology spectrum tries to point out.  Essentially, there are many different ways to express meaning, an ontology is just one of those ways.

The presentation is organized to answer three questions:

1. What is the difference between an Ontology and a:
2. When people say things like "Ontologies have unambiguous semantics", what do you think they mean; do you believe them; and why or why not.
3. How are ontologies and semantics relevant to industry today?

Ontology is defined in the textbook Ontology Engineering  by Elisa Kendall and Deborah McGuinness as follows:

Ontology - a model that specifies a rich description of the

• terminology, concepts, nomenclature;
• relationships among and between concepts and individuals; and
• sentences distinguishing concepts, refining definitions and relationships (constraints, restrictions, regular expressions)

      relevant to a particular domain or area of interest.

I reconcile that definition above to the common components of an ontology that I summarize in the document demystifying ontologies as follows:

• Terms
  • Simple terms (primitive, atomic)
  • Functional component terms (complex functional terms)
  • Properties (qualities, traits)
• Relations
  • Type relations (class/type relations, "type-of" or "is-a" or "class-subclass" or "general-special")
  • Functional relations (structural relations, "has-a" or "part-of" or "has-part" or "whole-part")
  • Property attribution (has property)
• Assertions
  • Restrictions (constraints, limitations)
  • Axioms
  • Rules (theorems)
• Individuals
  • Instance (facts)

This forms a formal, logical system that is:

• Consistent (no theorems of the system contridict one another)
• Valid (no false inference from a true premise is possible)
• Complete (if an assertion is true, then it can be proven; i.e. all theorem exists in the system)
• Sound (if any assertion is a theorem of the system; then the theorem is true)
• Fully expressed (if an important term exists in the real world; then the term can be represented within the system)

Fundamentally, Uschold points out, an ontology is "A way for a community to agree on common terms for capturing meaning or representing knowledge in some domain."  The ontology spectrum helps you understand to what extent you are actually agreeing. (Logic; Formal System)

And so, the reason for creating an "ontology-like thing" is to make the meaning of a set of terms, relations, and assertions explicit, so that both humans and machines can have a common understanding of what those terms, relations, and assertions mean.  "Instances" or "sets of facts" (a.k.a. individuals) can be evaluated as being consistent with or inconsistent with some defined ontology-like thing created by some community.  The level of accuracy, precision, fidelity, and resolution expressively encoded within some ontology-like thing depends on the application or applications being created that leverage that ontology-like thing.

An ontological commitment is an agreement by a community to use some ontology-like thing in a manner that is consistent with the theory of how some domain operates, represented by the ontology-like thing.  The commitment is made in order to achieve some goal established by the community sharing the ontology-like thing.

Ontology-like things for accounting, reporting, auditing, and analysis require high-quality and therefore they require highly expressive ontology-like things.

As Kendall and McGuinness point out, "The foundation for the machine-interpretable aspects of knowledge representation lies in a combination of set theory and formal logic."

Put these pieces together correctly and you can get software applications to perform magic! That is what curated metadata is all about. You need to use the right tool for the job. Financial reports will lead the way.

Might be time to increase your digital maturity.