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Understanding Knowledge Modeling

Per MAKHFI.COM Knowledge Modeling is

the concept of representing information and the logic of putting it to use in a digitally reusable format for purpose of capturing, sharing and processing knowledge to simulate intelligence. Among others it can address business matters such as Agility, Compliance, Consistent Decisioning, Reasoning and Knowledge retention.

In the simple form, a Knowledge Model would be designed with the purpose of receiving data produced from various sources and generate outputs that could trigger actions.

I would point out the phrase "simulate intelligence".  In terms of, say, financial reporting; the knowledge is in the form of what someone like a certified public accountant or external reporting manager knows about financial reporting.  Or, the resources that they use in the process of external financial reporting such as the accounting standards codification, a disclosure checklist, and other such resources. 

You can move this knowledge.  Someone who as the knowledge can put it into the form that a machine can use and then the machine can tie all these resources together and the machine can help you create a financial report.  The machine simulates the intelligence necessary to create a financial report by using the knowledge which was articulated in machine readable digital form.

You can check out that web site, lots of useful information.  I will point out two very helpful things.

First, on the introduction page that site provides another explanation of the difference between data, information, knowledge, and wisdom (or they use intelligence).

  • Data: The basic compound for Intelligence is data -- measures and representations of the world around us, presented as external signals and picked up by various sensory instruments and organs. Simplified: raw facts and numbers.
  • Information: Information is produced by assigning meaning to data relevant to mental objects. Simplified: data in context.
  • Knowledge: Knowledge is the subjective interpretation of Information and approach to act upon in the mind of perceiver. As such, knowledge is hard to conceive as an absolute definition in human terms.
  • Wisdom (or Intelligence): Intelligence or wisdom embodies awareness, insight, moral judgments, and principles to construct new knowledge and improve upon existing ones.

They provide the following examples to help you understand the difference. DATA: The numbers 100 or 5, out of context; INFORMATION: Principal amount of money: $100, Interest rate: 5%; KNOWLEDGE: At the end of Year I get $105 back; INTELLIGENCE: Concept of growth.

Second, the web site categorized information into two distinguishable types:

  • Explicit knowledge: Can be articulated into formal language, including grammatical statements (words and numbers), mathematical expressions, specifications, manuals, etc. Explicit knowledge can be readily transmitted to others. This type of knowledge can be easily "modeled" using various computer languages, decision trees and rule engines.
  • Tacit knowledge:  Personal knowledge embedded in individual experience and involves intangible factors, such as personal beliefs, perspective, and the value system. Tacit knowledge is hard (but not impossible) to articulate with formal language. Neural network offers the best possible method for modeling tacit knowledge.

Now, I would encourage you to go back and read this blog post: Digital Financial Reporting Will Change Accounting Work Practices.  Read the part about how CAD software works.  Project that into how digital financial reporting software will work.  If you still don't get it, try this video: Digital Is Not Software, It Is a Mindset. Be sure to watch the part about the Amazon.com warehouse with "robot storage shelves that move around."

Posted on Monday, March 24, 2014 at 12:12PM by Registered CommenterCharlie in | CommentsPost a Comment

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