Digital Financial Reporting

I updated my ontology spectrum graphic that I explained in another blog post. Here is the updated version:

 (Click image for larger view)

One thing worth noting is that a big shift occurs when relatins are defined formally as opposed to informally (the red zig zag in the diagram).

(Disjunction | Transitive | Is-a | Has-a | Part-of | Part relations | Logic gates | Cardinality | Class | Subclass)

As I said in the previous blog post, the graphic above was inspired by this graphic (see slide 4) and some of these other graphics.

In addition to updateing the graphic, I mapped that ontology spectrum to implementations in two technology stacks: XBRL and the Semantic Web Stack.  Recall this blog post where I point out the need for a unifying logic and compare/contrast the XBRL and Semantic Web stacks.

Here are the mappings from the ontology spectrum to:

• XBRL mapped to ontology spectrum
  • XBRL Specifications
• Semantic Web Stack mapped to ontology spectrum (This paper explains the implementation but the paper is 10 years old and does not reference SHACL; see this related to rules)
  • RDFS
  • SKOS
  • RDF | RDF XML | RDF Turtle | RDF N-Triples | RDFa  | JSON LD
  • RDF Data Cube Vocabulary
  • OWL 2
  • SWRL
  • SHACL
  • RIF | RIF and OWL compatability | RIF Framework for Logic Dialects | RIF Basic Logic Dialect
  • DCAT

As I understand it, it is the intent of the Standard Business Report Model(SBRM) to support either technology stack.  This is my view as to what the SBRM will end up looking like. I could be wrong, it is not complete.

What people tend to misunderstand is that the application you are trying to create picks where you need to be on the ontology spectrum.  You don't get to pick.  The level of quality, precision, accuracy, breadth, depth, and leads to a some required level of expressiveness that you need to make your application work effectively.  Impedance mismatches between what you need and what you provide causes problems.

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Class relations defined using XBRL

Properties defined using XBRL

AI is taxonomies and ontologies coming to life (NOT like humans learn)

See the section "Common Components of Ontologies" in this article

Demystifying the role of ontologies

Framework for evaluating ontologies

Ontologies and fault management (NASA, very good!)

AI - Externalization of Mind (Slide Share)

Demystifing Ontology