Knowledge Assembly

I ran across a new term, knowledge assembly, which is used to explain some important ideas related to the idea of a knowledge graph or knowledge graph system.  Similar terms are knowledge fabric or data fabric or data mesh or information mesh.  Allow me to explain.

Hetionet provides an example of what they refer to as a knowledge assembly.  Hetionet is a demonstration of connecting multiple databases of information (an “assembly") that contain data and meta data related to biomedicine.

A similar sort of knowledge assembly for a financial reporting scheme which is a subset of finance.  There is knowledge that is common to all financial reporting schemes such as the double entry bookkeeping model and the accounting equation. The logical model of a financial report is part of that assembly.  The conceptual framework of the 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.  How to compute financial ratios used to analyze information reported per that scheme are part of the knowledge assembly. A specific economic entity's report model is another part of the assembly.  The entire SEC EDGAR system database of reports could be part of the knowledge assembly, or similarly the ESMA database of reported financial information.is another part of the assembly.

Not provided to the regulators unless called for by a lawsuit or something,  the accounting working papers and audit schedules that support the financial report model and financial report.  That part of the knowledge assembly supports the economic entity's financial report.

Validation and verification is used to assure quality of information within a knowledge assembly.  Everything must be consistent, complete, and precise; there are no contradictions in the system.

I provides examples of what amount to a knowledge assembly in my examples of different general purpose financial report creation schemes.  All of my examples use the same framework which I refer to as the Seattle Method.  That framework is heavily tested and is proven to work effectively.

My framework uses the global standard XBRL to represent the knowledge graphs within a standards-based knowledge graph system using a standards-based framework (Seattle Method, Standard Business Report Model) that make up the knowledge assembly.  Other machine readable representation approaches of such a knowledge assembly exist such as the Semantic Web Stack (RDF+OWL+SHACL and then use SPARQL to query information) or a graph database (using GSQL) or PROLOG (modern ISO standard PROLOG) or other such similar robust standards-based mechanisms.

A knowledge assembly is a set of knowledge graphs that, when processing, is seen as one unitary graph. A knowledge graph is a machine-readable structured representation of knowledge (semantics) related to a particular area of interest.  So a knowledge assembly is a machine-readable network of things and relations between things.  The things and relations 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 = ontology (things and relations between things) + rules (assertions, restrictions, constraints).  A knowledge assembly can be explained using a logical theory or logical schema that verifies/validates the knowledge assembly.  Knowledge assembly terminology is grounded in the more approachable and innately understandable terminology of logic and philosophy, not the technical jargon/terminology of computer science.

What always seems to be necessary to work with some machine readable knowledge assembly is:

  1. Some sort of database to store the knowledge within.
  2. A logical model that is used to understand and process the information within that knowledge assembly.
  3. Some sort of reasoning engine or semantic reasoner or rules engine that understands the logical model (#2) and processes the information in the database (#1) to give you the answers that you need from the knowledge stored in the knowledge assembly.
This capability, when implemented effectively, is an incredibly useful tool.  This can be a general tool, think semantic spreadsheet or a specific tool for financial reporting.  But you need to make it work like a well oiled machine for the capability to be useful.


Additional Information:

Comments

Post a Comment

Popular posts from this blog

Relational Knowledge Graph System (RKGS)

Image
Relational Knowledge Graph System (RKGS) is an interesting idea that combines the relational paradigm and the knowledge graph paradigm.  This documentation, Why RKGS , explains the idea. This gets even more interesting with the notion of "relational AI".  RelationalAI is described as follows by the company of the same name, Relational.ai : “RelationalAI is a cloud-based relational knowledge graph management system, with state of the art probabilistic processing and declarative reasoning at scale to make developing Data Applications a superpower for your business.” This notion of knowledge graphs represented in relational databases fits into my belief that knowledge graphs have two aspects: the expression SYNTAX and the logic of what is expressed or SEMANTICS. While the LOGIC of a knowledge graph is always the same regardless of technical syntax used to express that logic; there are many, many different technical syntaxes that can be used to represent a knowledge graph. There
Read more

Graph Hairball

Image
Knowledge graph system logic, the "things" and "relations between things" that  graph theory calls "vertices" (a.k.a. nodes, points, entities, things) and "edges" (a.k.a.  links, lines, relations, associations), looks like a big graph hairball as some people call it an example of which you can see here: Why?  This common view of a graph is because there has to be some "general view" of the information provided by the graph.  This is just like a general view of the information within a relational database is a table ; a set of ROWS and COLUMNS for exactly one table at a time. So what is interesting is that with a graph database the default view is everything in the graph of knowledge.  Whereas the default view of everything in a relational database is one table at a time. Further, it does not really matter whether the information that populates your knowledge graph comes from, say, CSV files, Excel, JSON, XBRL, RDF, PROLOG or whateve
Read more

PLATINUM Business Use Cases, Test Cases, Conformance Suite

Image
A brick wall is made of exactly two things: bricks, mortar. But a brick wall created by a master craftsman, or mason, and a brick wall created by a “weekend warrior” with no knowledge of masonry will be very different. Master craftsmen are created and that process takes time and effort. This blog post is a first prototype that brings several things together into one useful resource for business professionals.  It pulls together the human readable information of the Showcase of Reports   and the machine readable information of the Conformance Suite of XBRL-based Financial Reports Prepared using the Seattle Method .  Then, I want to incorporate the information found in these PLATINUM XBRL-based Financial Report Examples . The three resources can be useful and used independently; but they are even more useful when used together.  Business professionals really could not care a less about the technical details of getting some result that they want.  But technical professionals need to under
Read more