Knowledge model

Definition

A Knowledge model is A representation of knowledge used to understand and communicate an aspect of knowledge in the real world.

Description

Numerous knowledge models exist to describe different important aspects of knowledge for the purposes of understanding and communication. A number of models which are commonly used in describing knowledge are described here. The models emphasize different elements of knowledge and represent them at different levels of complexity.

In the same way that an automotive vehicle may be described in terms of many dimensions such as:

1. Materials: Metal, Rubber, Glass etc.
2. Components: Wheels, Engine, Passenger compartment, etc.
3. Colours : Black, Red, Grey etc.

So also can Knowledge be described in terms of a number of different dimensions such as:

1. Domains – technical, organisational, societal etc.
2. States – Explicit, implicit, tacit etc.
3. Levels – Know-why, know-how, know-what etc.
4. Representations – Documents, models, pictures etc.
5. Categories – Good practices, lessons, standards etc.
6. Artefacts – Buildings, tools, equipment, etc.

Some models illustrate only the major components of knowledge, others also illustrate systems, subsystems and even processes. This article describes some of those that have been used in IAEA publications in an ascending order of complexity.

The models can be classified into three major groups.

1. Component models: where knowledge is described as being composed of two or more parts
2. Heirarchical models: where knowledge can be processed into different types with increasing rarity, value and quality
3. Subjugated models: where the definition of knowledge is narrowed and described as a subset of something more significant such as "Wisdom" or "Competence"

Component models

Tacit-Explicit spectrum models

Fig.1. Tacit - explicit spectrum models

Fig 2. The Iceberg model of tacit and explicit knowledge

It is useful to identify three types of knowledge: Explicit, tacit and implicit. Each requires different approaches to its management. The model is often illustrated with reference to an iceberg (Fig. 2)

• Explicit is Knowledge that has been articulated or has already been codified in some form
• Implicit is Implicit knowledge is held in a person’s mind; it is the knowledge which has not yet been captured or transferred in any form. Knowledge that people carry in their heads.
• Tacit is The knowledge wholly embodied in the individual and is rooted in practice, experience, intuition and individual skills that is difficult or even impossible to recall, articulate and thus to transfer.

The models cover the spectrum from tacit knowledge, i.e. the knowledge that resides within a person, and explicit knowledge, that has been articulated or has already been codified in some form.

A simplified, but popular model is the Iceberg model, which depicts the relationship between explicit and tacit knowledge - the implicit knowledge is subsumed under tacit knowledge, a simplification often found in the literature.

In some more detailed models, explicit knowledge is further divided into codified and non-codified explicit knowledge, referring to knowledge which is already codified, or knowledge which could be codified. However, this distinction is not often made and of limited usefulness.

A priori, a posteriori knowledge model

A priori knowledge is independent of experience (theoretical knowledge).

A posteriori knowledge is dependent on experience or empirical evidence (practical knowledge).

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  Fig.6. A priori, a posteriori knowledge model

Hierarchical models

Data, information, knowledge, (wisdom) model

Raw data become information in the context of creation, information becomes knowledge in the context of use, meaning that a human agent with the appropriate background is required. Sometimes, the data, information, knowledge model is extended with the level of wisdom, as knowledge may eventually lead to wisdom (http://en.wikipedia.org/wiki/Wisdom).

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  Fig.6. Data, information, knowledge model

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  Fig.7. Data, information, knowledge, wisdom model

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  Fig n. Transformational actions for data, information and knowledge

Know-how, Know-why, Know-what models

This is sometimes referred to as the knowledge level model. Know-how usually refers to a synonym for skill. This means A learned ability to do something wellKnow-what is referring to the facts about a certain subject.

Know-why refers to the knowledge about  theoretical knowledge and an understanding of causal relationships, interactive effects and uncertainty levels associated with a given situations which can be used to find new solutions and solve problems.

. A simple example to illustrate this is the need for different types of knowledge when jumping into deep water. Know-how is the ability to swim, know-what is knowing what arm and leg actions are required as they may be presented in a written instruction manual on swimming. Know-why may include an understanding of Archimede's principle and Newton's third law.

This model can be extended to include other aspects of knowledge. As well as know-how, know-why, know-what, we can add know-who, know-when, know-where.

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  Fig.7. Know-why,know-how,know-what knowledge model

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  Fig.8. Know-why,know-how,know-what,know-who,know-when,know-where knowledge model

Proficiency, codification, diffusion model

Description

Knowledge has three attributes whose values can be managed by knowledge management. These three attributes are:

1. level of proficiency (P)
2. level of codification (C)
3. level of diffusion (D)

Proficiency refers to how much of a particular capability a person or team has. It is the degree of mastery of a skill or area of knowledge. Codification refers to the transformation of knowledge into a human-readable format. Diffusion refers to the number of people who hold knowledge in any given area and the means to spread that knowledge.

Domains knowledge model

Description

Fig. 2. Domains knowledge model

All knowledge can be subdivided onto knowledge domains. Each is the content of a particular field or area of knowledge. In knowledge management domains are often defined by the scope of Communities of practice, taxonomies or Educational courses and are useful subdivisions of knowledge into which to organise knowledge repositories.

Example

At a macro scale, examples of domains might be Chemistry, Physics, Biology or at a smaller scale the domain of Physics may be subdivided into many more domains such as Classical Mechanics, Electricity and Magnetism, Modern Physics, Optics, Thermodynamics etc. A list of nuclear knowledge domains can be found in List of nuclear knowledge domains.

Practical application in KM

Practical tools for managing domains include ontolgies which describe knowledge of a specific domain through concepts in their relation to other concepts.

References

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