Global Standard Knowledge Assembly

My PROOF (and my other examples) provide examples of a global standard XBRL-based knowledge assembly of a financial reporting scheme, a financial report model created by a reporting economic entity using that financial reporting scheme, and a financial report using that financial report model that is based on the financial reporting scheme.  That entire knowledge assembly is validated using a rules engine that is specialized for this specific type of knowledge assembly to verify that the report and report model are complete, consistent, and precise. This mechanism is described by the Seattle Method.

That knowledge assembly contains data and meta data related to financial accounting and financial reporting.   The physical syntax of the entire knowledge assembly is global standard XBRL.  The XBRL technical syntax is used to define logical terms, structures, associations, assertions, restrictions, constraints, and facts.

The logical model of a financial report is part of that assembly.  Key portions of the conceptual framework of a financial reporting scheme is a part of the knowledge assembly.  The different reporting styles permitted by that financial reporting scheme is part of the knowledge assembly.  Wider-narrower (a.k.a. type-subtype or general-special) relations are part of the knowledge assembly.  Fundamental accounting relations that are universal to a specific reporting style is part of the knowledge assembly.  How to compute financial ratios used to analyze financial information reported per that financial reporting scheme are part of the knowledge assembly. Rules are declarative in nature.

A specific economic entity's report model is also another part of the knowledge assembly.  Each individual XBRL-based financial report submitted to the Securities and Exchange Commission (SEC) could be part of the knowledge assembly.  Similarly, the European Single Market Authority (ESMA) set of XBRL-based financial reports could be part of the knowledge assembly.

All of the above is described both logically and physically/technically by the knowledge assembly.  But accountants only need to interact with the logical representation; the technical aspects including all the details of the global standard XBRL technical syntax are hidden from accountants, but are there for the computer scientists to build software.  Accountants, auditors, and analysts need only understand the logic of accounting and reporting to interact with the knowledge assembly using software built specifically for that purpose.

A knowledge assembly is a set (a.k.a. collection) of knowledge graphs. A knowledge graph is a machine-readable structured representation of knowledge (semantics) in the form of a directed acyclic typed graph related to a particular area of interest; in our case the area of interest is financial accounting and reporting.  

A knowledge assembly is a machine-readable network (network theory) of things and relations between things represented as a set (collection) of machine-readable graphs that are also human-readable after the machine-readable information has been rendered using software.  The things and relations (semantics) are classified or grouped in helpful/useful ways.

Semantics is the science of giving meaning to data.  Knowledge assemblies are about semantics which is data in context, a.k.a. information. 

Knowledge is more than just an ontology.  Knowledge = ontology (things and relations between things) + rules (assertions, restrictions, constraints).  

A knowledge assembly can be explained using a logical theory and enforced using a logical schema that verifies/validates the contents of the knowledge assembly.

A knowledge assembly is a logical system.  Knowledge assembly terminology, described by a logical conceptualization, is grounded in the more approachable and innately understandable terminology of the branch of logic which is part of the discipline of philosophy, rather than the technical jargon/terminology of computer science which is less approachable to business professionals. And to be clear, we are not working with "data"; we are working with "information" which is data in context. Using information, a skilled and experienced craftsperson can create knowledge which leads to insight which leads to wisdom. (Credit for the graphic below)


These global standard knowledge assemblies will be the basis for computational professional services. New tools such as the modern spreadsheets and modern business intelligence software will be used for working with information using machines that augment the skills of professional accountants much like a calculator augments ones ability to do math. Explainable artificial intelligence (XAI) will power these new tools.

This explainable artificial intelligence will come in different forms.  A hybrid of different types of artificial intelligence will drive computational professional services.  There is no "one-size-fits-all" artificial intelligence that will magically solve all problems.  The right type of artificial intelligence approach will be used for the job.  These knowledge assemblies will be created by skilled and experienced professionals that weave together these knowledge assemblies.  These rule-based knowledge assemblies (deductive reasoning) will be the basis for or the "training data" for enabling machine learning (inductive reasoning, abductive reasoning).

Make no mistake: weaving all this together elegantly into easy to use software that provides useful functionality that is approachable to business professionals is a not trivial task.  To make this work, the following is necessary:

  1. Logical model(s).
  2. Physical/technical syntax to exchange information and get things into and out of the database.
  3. Databases containing information (data plus context).
  4. Assertions, constraints, restrictions represented in machine-readable form stored in the database to enforce the logical model and keep quality high.
  5. Query/rules/semantics processing "logic engine" that understands the logical model (#1) and can read the database (#3) can read the rules (#4).
  6. GUI/UX that expose this functionality to business professionals on their terms. (See the worlds first software tool that achieves this; or experience this tool for yourself)
  7. APIs that drive the GUI/UX.

Bonus points for enhancing the above by leveraging digital distributed ledgers to provide for tamper proof audit trails, cryptography to create functionality such as Merkle proofs, and other necessary pieces that enable verifiability, trust, and proof.  More bonus points, perhaps, if some cryptocurrency is used to enable a value exchange mechanism within some sort of system.

All of the above will modernize the universal technology of accountability, making it more effective for the information age. New tools will be created for accounting and audit. Key to this is the effective management of semantic hygiene.

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