Knowledge structure

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Knowledge structure
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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

  • Ontologies: Ontologies are formal representations of knowledge that represent different types of real-world objects and the relationships between them. For instance, a medical ontology might describe different types of diseases and their symptoms, as well as how they are related to each other.
  • Concept maps: Concept maps are graphical representations of a domain of knowledge that illustrate the relationships between concepts. For instance, a concept map of a computer system might illustrate the different components of the system, their interrelationships, and how they interact with each other.
  • Taxonomies: Taxonomies are hierarchical classification systems that organize knowledge into groups based on shared attributes. For instance, a taxonomy of animals might classify them into different categories based on their physical characteristics, such as mammals, reptiles, and birds.
  • Semantic networks: Semantic networks represent knowledge as a network of interconnected nodes and edges, where each node represents a concept and each edge represents a relationship between two concepts. For instance, a semantic network of a family tree might include nodes for each person in the family and edges that indicate the relationship between them, such as parent and child.

Advantages of Knowledge structure

Knowledge structure provides a number of advantages, including:

  • Increased understanding of the organization of information, allowing for more efficient retrieval of needed data.
  • Ability to create a framework that allows for multiple interpretations of data, thus increasing flexibility and adaptability.
  • Improved accuracy of data analysis due to the ability to identify relationships between sets of data.
  • Enhanced organization of data, which can lead to improved decision-making processes.
  • Improved speed of data retrieval, due to the ability to store data in a structured form.
  • Enhanced reliability of data analysis by allowing users to easily identify patterns and trends.
  • Improved security of data, as data can be securely stored and accessed using a knowledge structure.

Limitations of Knowledge structure

Knowledge structure is a passive scheme under which knowledge can be organized and managed, however it has certain limitations. These limitations include:

  • An inability to draw on outside sources for further information. A knowledge structure is limited in scope, and can only offer knowledge that is available in its own database.
  • It is also limited in its ability to recognize patterns and learn from experience. Knowledge structures are not able to reason or draw conclusions from the data they store, and as such cannot draw on previous knowledge to inform current decisions.
  • Lastly, knowledge structures are not able to adapt to changing circumstances. If a new environment or context arises, the structure may not be able to apply the stored knowledge to the new situation.

Other approaches related to Knowledge structure

One-sentence introduction: Apart from the Knowledge structure there are several other approaches that can be used to organize and manage knowledge.

  • Ontologies: Ontologies are formal models of shared understanding and terminology used to represent and share knowledge. They are used to create a shared view of the world and to provide a common language for communication.
  • Semantic Networks: Semantic Networks are networks that represent the meaning of words and phrases by linking them together in a graph-like structure. The nodes in the network are concepts and the edges are the relationships between them.
  • Knowledge Maps: Knowledge Maps are visual representations of knowledge, usually in the form of diagrams or charts. They can be used to illustrate relationships between concepts and ideas, and to create a shared understanding of a particular domain.
  • Expert Systems: Expert Systems are rule-based systems that employ a knowledge base of facts and rules to solve problems in a specific domain. The knowledge base is used to determine the best solution to a problem based on the available evidence.
  • Natural Language Processing: Natural Language Processing is a field of artificial intelligence that deals with the understanding of natural language text by machines. It is used to automatically extract knowledge from text, and to generate natural language text from a given knowledge structure.

To summarize, Knowledge structure is an important method for organizing and managing knowledge, but there are also several other approaches, such as Ontologies, Semantic Networks, Knowledge Maps, Expert Systems and Natural Language Processing, that can be used to represent and share knowledge.

References

Author: Izabela Stań

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