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Knowledge Modeling

Most managers, when asked, will say that their most important asset is their people. Of course, they’re not talking about flesh and bones... they’re talking about minds.

We use the term "knowledge" very loosely with dozens of definitions for it, many of which make rather vague distinctions among data, information, knowledge, and intelligence.

Here I like to cite A.C. Foskett’s distinction between knowledge and information:
               "Knowledge is what I know, Information is what we know."

Before getting into more detail, I like to establish a common vocabulary and share my definition of related terms, which may vary from other publications.

The concept of Intelligence is built upon four fundamental principles:
      Data, Information, Knowledge, and Wisdom or Intelligence.

  < POLL >  

How has the general rate of Innovation changed over the last 100 years?

Increased
Decreased
Not changed

Results Past Polls


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 is produced by assigning meaning to data relevant to mental objects. Simplified: data in context.

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.

Intelligence or wisdom embodies awareness, insight, moral judgments, and principles to construct new knowledge and improve upon existing ones.

Following Bank example would illuminate the definitions:
    - 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


We are living in an era of ever-increasing pace of technological advancement.
Beside increasing the performance and reducing the size, we are left with only one possible progress path . . . a move toward more “knowledgeable machines” (I would rather use the term “knowledgeable” instead of “intelligent” for the next few years!).

The more knowledge available to machines, the more automation can be achieved and the closer we get to true intelligent systems.

Nothing is more valuable than putting information to effective use in an automated manner and that can only be achieved by deploying more structural knowledge into our technical life.

As such the most important area in computer industry in the coming years would be the means to deal with knowledge as matter, referred to as "Knowledge Modeling".

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 (baby boomers retirement, development going offshore,...).
Although unbelievable today, I can clearly envision a day when we are able to perform a full-blown modeling of a person’s entire knowledge base. That operation would be referred to as “SoulByting”, which is outside the scope of this website. Now back on earth ...

Following diagram (courtesy of VentureChoice.com) illustrates a sample knowledge model for venture capital decision process:


The most common applications of knowledge modeling are used for education, decision support, alerting and automation. In marketing and consumer research space knowledge modeling is utilized to model the decision process of an individual or segment in a particular context (referred to as Choice Modeling).
There are already established techniques and methods to facilitate Knowledge Modeling activities, such as data mining for knowledge discovery, collaboration tools and document management systems for Knowledge sharing and rule engines, BPMS and Expert Systems to capture, process, simulate or react the knowledge inquiries.

In general, knowledge can be categorized 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.


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.

Knowledge models can be implemented as software applications, hardware components, object library, web-services, and many other forms and shapes using various techniques. The closest natural reassembled technique to human brain for modeling knowledge is Neural Network.

As a bridge between academic and business world, MAKHFI.COM intends to help people understand and apply this unimaginably powerful science to their everyday projects.

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