A corporation’s success depends on its intellectual capital. Knowledge modeling offers a powerful means for capturing, organizing, and sharing the collective expertise of employees. Knowledge modeling is a method to get expertise to the right people at the right time by leveraging process, content, people, and technology. Business principals and managers gain a comprehensive understanding of the structure of knowledge and the technologies that can facilitate its use in an organization, and learn strategies for effectively capturing organizational knowledge. Knowledge Modeling provides knowledge engineers the framework in which to identify types of knowledge and structure that knowledge to be used in a myriad of applications including Knowledge Management Systems, Knowledge Graphs, IoT and Machine Learning.
Knowledge modeling has emerged as one of the major achievements from the field of artificial intelligence. Knowledge engineers can graphically represent knowledge in a variety of ways based on the type of knowledge being depicted. By conceptualizing aspects of the domain, it enables the knowledge engineer to readily see how tasks are performed and problems are solved. A well diagramed domain makes the task of communicating to subject matter experts and non-experts less of an issue.
However, the type of knowledge one encounters further complicates the task of knowledge modeling. Due to the fact there are many classifications of knowledge the appropriate modeling method used to capture specific knowledge will change from one form of knowledge to another. The type of knowledge that must be captured will fall into one or more classifications. The following represents several classifications of knowledge that can be captured:
Declarative Knowledge – Knowledge of Facts
Procedural Knowledge – Knowledge of how to do things
Tacit Knowledge – Knowledge contained within humans and cannot be articulated easily
Explicit Knowledge – Knowledge contained in documents, computer programs, databases, etc. and which can be articulated easily
Process Knowledge – Knowledge contained in processes
Concept Knowledge – Knowledge contained in concepts
The knowledge gathered by the knowledge engineer more often will be a combination of one or more of the knowledge classifications listed above. In the process of developing the knowledge model, the knowledge engineer will identify several “low-level” knowledge objects These “low-level” knowledge objects include concepts, instances, processes, attributes and values, rules, and relationships.
There is a myriad of ways in which to model knowledge. Therefore, a thorough understanding of how knowledge can be represented is needed in order to accurately capture the knowledge of a domain. Here are four (4) major representations of knowledge. These representations include Ladders, Concept Map, Process Map, and Decision Trees.
Ladders: Ladders are hierarchical (tree-like) diagrams. Some important types of ladders are concept ladder, composition ladder, decision ladder, attribute ladder and process ladder. Laddering provides a way to efficiently validate the knowledge of the domain.
A concept ladder shows classes of concepts and their sub-types. All relationships in the ladder are the is a relationship, e.g. car is a vehicle. A concept ladder is more commonly known as a taxonomy and is vital to representing knowledge in almost all domains.
A composition ladder shows the way a knowledge object is composed of its constituent parts. All relationships in the ladder are the has part or part-of relationship, e.g. wheel is part of car. A composition ladder is a useful way of understanding complex entities such as machines.
A decision ladder shows the alternative courses of action for a particular decision. It also shows the pros and cons for each course of action, and possibly the assumptions for each pro and con. A decision ladder is a useful way of representing detailed process knowledge.
An attribute ladder shows attributes and values. All the adjectival values relevant to an attribute are shown as sub-nodes, but numerical values are not usually shown. For example, the attribute color would have as sub-nodes those colors appropriate in the domain as values, e.g. red, blue, green. An attribute ladder is a useful way of representing knowledge of all the properties that can be associated with concepts in a domain.
This ladder shows processes (tasks, activities) and the sub-processes (sub-tasks, sub-activities) of which they are composed. All relationships are the part of relationship, e.g. boil the kettle is part of make the tea. A process ladder is a useful way of representing process knowledge.
A concept map is a type of diagram that shows knowledge objects as nodes and the relationships between them as links (usually labeled arrows). Concepts are represented in a hierarchical fashion with the most inclusive, most general concepts at the top of the map and the more specific, less general concepts arranged hierarchically below. The hierarchical structure for a particular domain of knowledge also depends on the context in which that knowledge is being applied or considered. Therefore, it is best to construct concept maps with reference to some particular question we seek to answer or some situation or event that we are trying to understand through the organization of knowledge in the form of a concept map.
Another important type of network diagram is a process map. This type of diagram shows the inputs, outputs, resources, roles and decisions associated with each process or task in a domain. The process map is an excellent way of representing information of how and when processes, tasks and activities are performed.
A process is a transformation; it transforms its inputs into its outputs. It is a picture showing how the transformation is carried out. It shows the inputs and outputs (best described using nouns), the activities in between (best described using verbs), and for each of the activities, the inputs and outputs used and produced. A process is not just about ‘what people do’, but also ‘what people produce’. Historically, there has been a lot of emphasis attached to the study of the way people performs their jobs, i.e. the activities they carry out, or the verbs in the process map.
A good process map should, allow people unfamiliar with the process to understand the interaction of causes during the workflow. Also a good process map should contain additional information relating to the project (i.e. information per critical step about input and output variables, time, cost, etc.), be understood at various levels of the organization, be able to model complex activities without ambiguity, be effective in analyzing a process and identify process related issues.
A decision tree is an arrangement of tests that prescribes an appropriate test at every step in an analysis. In general, decision trees represent a disjunction of conjunctions of constraints on the attribute-values of instances. Each path from the tree root to a leaf corresponds to a conjunction of attribute tests, and the tree itself to a disjunction of these conjunctions. More specifically, decision trees classify instances by sorting them down the tree from the root node to some leaf node, which provides the classification of the instance. Each node in the tree specifies a test of some attribute of the instance, and each branch descending from that node corresponds to one of the possible values for this attribute.
A decision tree creates a model as either a graphical tree or a set of text rules that can predict (classify) a given situation. A decision tree is a model that is both predictive and descriptive. It is called a decision tree because the resulting model is presented in the form of a tree structure. The visual presentation makes the decision tree model very easy to understand and assimilate. As a result, the decision tree has become a very popular data mining technique.
More can be found on Knowledge Modeling in the book: UML for Developing Knowledge Management Systems