Knowledge structure

Knowledge structure
See also
Organizational systems Research strategy Areas of knowledge Types of knowledge Project Innovative thinking Principles of design harmony Consumer orientation Nature of strategic management

Knowledge structure is a passive scheme under which knowledge can be organized and managed, unlike the reasoning mechanism, that actively manipulates data to get the desired output such as an answer. (J. Liebowitz 1998, s. 90)

Knowledge classification

Knowledge can be classified depending on its goal as well as its formality (Becerra Fernandez 2014, s. 24-25).

Declarative and procedural:

• Declarative knowledge explains relationships and correlations between particular variables. It is determined by facts and can be easily codified. Beccera Fernandez notes that “Declarative knowledge can be stated in the form of propositions, expected correlations, or formulas relating concepts represented as variables.”. In business, it might help to identify for instance what kind of product is on demand in a certain group of clients.
• Procedural knowledge shows what actions or steps should be taken to complete the desired task or achieve a particular outcome. This type of knowledge is used mainly by task automation, means that activities are performed in the most efficient way without taking conscious actions.

Tacit and explicit:

• Tacit knowledge covers personal insights and intuition, which are based on own experience and memories. Thus it is difficult to be formalized or verbalized. This type of knowledge is used in business for instance when making some predictions based on previous observations of a certain market/industry.
• Explicit knowledge is presented in the form of numbers and words and verbalized for example in manuals, patens, programs, graphs, etc.

General and specific:

• General knowledge is possessed by a larger group of individuals and can be easily transferred among them.
• Specific knowledge is limited to a small group of people, who are more knowledgeable about a particular matter.

In addition to the classifications above, Nathan Roberts (2019) names yet another knowledge type - structural knowledge, which is considered as a base for problem-solving activities. It is crucial by creating business strategies and determining requirements as well as conditions of particular procedures.

Structuring of business process-oriented knowledge

Knowledge, which is oriented on business process, can be structured in five main steps (K. Mertins 2003, s. 124):

• Shaping a chosen business process with the crucial knowledge bases with the reference to already existing knowledge structures
• Setting users' requirements for this particular knowledge structure
• Structuring relevant knowledge as the most important knowledge objects
• Formalizing the structure in consensus with experts as well as managers
• Introducing the knowledge structure with the reference to the maintenance processes

Knowledge Structure Mapping

Knowledge Structure Mapping enables to organize and visualize the organizational knowledge resources. These knowledge resources are necessary to perform tasks and activities within the organization. The main goal of knowledge structure mapping is to present the resources in a concise and precise way, so that they can be accurately analysed by experts and managers.

There can be distinguished five types of knowledge maps (M. J. Eppler 2004, s. 192 - 193):

• Knowledge source maps
• Knowledge asset maps
• Knowledge structure maps
• Knowledge applications maps
• Knowledge development maps

Examples of Knowledge structure

• Hierarchical Structure: A hierarchical structure is a type of knowledge structure where information is organized in a top-down, parent-child relationship. This structure is commonly used to represent the taxonomy of a particular field. For instance, in the field of biology, the hierarchical structure can be used to display the various species within the Animal Kingdom, such as mammals, fish, reptiles, and amphibians.
• Network Structure: A network structure is a type of knowledge structure that links various concepts together. This structure is commonly used to represent complex relationships between concepts and ideas. For instance, a network structure can be used to represent the various connections between people within an organization, such as employees and their colleagues or supervisors.
• Semantic Structure: A semantic structure is a type of knowledge structure used to represent the meaning of words. This structure is commonly used to represent the relationship between words and their definitions. For instance, a semantic structure can be used to represent the relationship between a word and its definition, such as a definition of a person or a definition of an object.

Advantages of Knowledge structure

Knowledge structure is beneficial in many ways. It can be used to effectively organize and manage knowledge in an efficient and systematic way. Below are some of the advantages of using knowledge structure:

• It enables easy retrieval of information when needed, since it is organized in a structured way.
• It facilitates learning and understanding of complex concepts since they can be broken down into smaller, more manageable pieces.
• It encourages collaboration and sharing of knowledge across individuals and organizations, allowing for more effective decision making.
• It provides a framework for understanding and analyzing data, allowing for more accurate and informed decisions.
• It can help to identify relationships between different pieces of information, allowing for better insight into the data.
• It can assist in developing a comprehensive system of knowledge management, which can be used to support decision making and problem solving.

Limitations of Knowledge structure

Knowledge structure has its own limitations. These include:

• Limited capacity for abstraction – Knowledge structure is limited in its ability to abstract from individual facts and create meaningful generalisations. It is not possible to represent abstract concepts and relationships that are not explicitly encoded in the structure.
• Difficult to update – Once a knowledge structure is created, it is difficult to update and maintain. This means that the knowledge must be constantly monitored and updated to ensure accuracy and relevance.
• Rigid – Knowledge structure is often rigid and inflexible, which makes it difficult to adapt to changing circumstances and contexts.
• Too much reliance on symbols – Knowledge structure relies heavily on symbols, which can be difficult to interpret and use. As a result, the structure may be difficult to understand and use effectively.

Other approaches related to Knowledge structure

One approach related to Knowledge structure is Artificial Intelligence (AI). AI is the application of computer science and engineering to create intelligent machines that can think and learn. It is used to simulate human behavior and make decisions based on data. AI can be used to solve complex problems, automate tasks, and create predictive models.

Another approach related to Knowledge structure is Ontology. Ontology is a set of concepts and relationships between them that describes a domain of knowledge. It can be used to represent knowledge in an organized way and to facilitate reasoning and communication.

The third approach related to Knowledge structure is Rule-Based Systems. Rule-based systems are software applications that use a set of rules to determine the behavior of a system. These rules are encoded in a computer program and can be used to automate decision making and task execution.

Finally, Knowledge Representation is another approach related to Knowledge structure. Knowledge representation is the process of representing knowledge in a formal language that can be understood by a computer. It is used to represent and reason with complex information.

In conclusion, Knowledge structure is a passive scheme under which knowledge can be organized and managed. Other approaches related to Knowledge structure include Artificial Intelligence, Ontology, Rule-Based Systems, and Knowledge Representation. These approaches are used to represent and reason with complex information and facilitate decision making and task execution.

References

• Becerra-Fernandez I., (2010). Knowledge Management: Systems and Processes, Upper Saddle River: Pearson Education Inc., Nowy Jork
• Eppler M. (2004)., Making Knowledge Visible through Knowledge Maps: Concepts, Elements, Cases, "Handbook on Knowledge management 1: Knowledge Matters", s. 189-205
• Gordon J. (2001)., Creating knowledge structure maps to support explicit knowledge management
• Liebowitz J. (1998)., Knowledge Organizations: What Every Manager Should Know, CRC Press
• Maier R. (2007)., Knowledge Management Systems: Information and Communication Technologies for Knowledge management, Springer Science & Business Media
• Mertins K. (2003)., Knowledge Management: Concepts and Best Practices, Springer Science & Business Media
• Roberts N. (2019)., Knowledge types
• Tyulkova N. (2019)., A Flexible Organizational Structure as a way of Knowledge Management in SMEƐ

Author: Izabela Stań