COGNITIVE MAP
Cognitive / knowledge map represents from "mental model" of one's own knowledge and provide a better form of codified knowledge. Mental models to explain the process when a person thinks and reasonable in complex environments. Cognitive map is the most powerful way to support the captured knowledge because it also means to capture the context and the complex relationships between the different key concepts. |
| Cognitive Map |
DECISION TREE
The decision tree is a system or way that humans developed to help locate and make a decision on these issues and to take into account various factors that exist within the scope of the problem. In general, a decision tree is a modeling picture of a subject that is composed of a series of decisions that leads to a solution.
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| Decision Tree |
Terminology in the decision tree:
1. Children (Child) or Parent (Parents). Description: b, c, and d is the son of a. And A is the parent of b, c, and d.
2. Tracks (Path). Description: The path from a to j is a, b, e, j. The path length from a to j is the number of edges traversed, namely 3.
3. Siblings (Sibling). Description: b, c, and d are siblings, because all three have the same parents, namely a.
4. Degrees. Description: number of children there on that node - a third degree and second degree b.
5. Leaves. Description: is a node that has no children - example: c, f, g, h, i, and j is a leaf.
6. In the Node (Internal Nodes). Description: The nodes that have children. Vertices a, b, and d is the knot in.
7. Level (Level). Description: 1 + length of the path from the node to the top node. The top node has level = 1
8. Trees n-ary. Description: tree branches that each node has the number n for children called n-ary tree. If n = 2, the tree is called a binary tree.
1. Children (Child) or Parent (Parents). Description: b, c, and d is the son of a. And A is the parent of b, c, and d.
2. Tracks (Path). Description: The path from a to j is a, b, e, j. The path length from a to j is the number of edges traversed, namely 3.
3. Siblings (Sibling). Description: b, c, and d are siblings, because all three have the same parents, namely a.
4. Degrees. Description: number of children there on that node - a third degree and second degree b.
5. Leaves. Description: is a node that has no children - example: c, f, g, h, i, and j is a leaf.
6. In the Node (Internal Nodes). Description: The nodes that have children. Vertices a, b, and d is the knot in.
7. Level (Level). Description: 1 + length of the path from the node to the top node. The top node has level = 1
8. Trees n-ary. Description: tree branches that each node has the number n for children called n-ary tree. If n = 2, the tree is called a binary tree.
KNOWLEDGE TAXONOMY
Most of unstructured information in the enterprise is information that is not managed, by 90% (Source: Gartner, 2005). This resulted in some problems such as difficulty finding and accessing information, security weaknesses, duplicationof information and others. Survey of IDC to 706 knowledge workers (knowledge worker), shows that the average knowledge worker spends too much time to find and process information, with a relatively high cost. Solution: build Knowledge Taxonomy. Knowledge Taxonomy is structured knowledge representation and in a form such that it reflects the expertise and knowledge of a specific field within the organization.
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| Knowledge Taxonomy Example |
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