knowledge representation methods
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Data, Information, and Knowledge definitions
 Different Types of Knowledge
 What is Knowledge Representation
 Alternative knowledge base representations

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1.Data
- Definition: Data refers to raw, unprocessed
facts, figures, or symbols that lack context or
meaning.
- Characteristics:
- Raw and unorganized.
- Lacks context or interpretation.
- Can be quantitative (e.g., numbers,
measurements) or qualitative (e.g., text, images).
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1.Data
- Examples:
- "25," "New York," "98.6°F," "0, 1, 1, 2, 3, 5."
- Role: Serves as the foundational input for
analysis and processing.
- Storage: Stored in databases, spreadsheets, or
files.
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2.Information
- Definition: Information is data that has been
processed, organized, or structured to provide
context and meaning.
- Characteristics:
- Data with context or relevance.
- Answers basic questions like "who," "what,"
"where," and "when."
- More useful than raw data but still not
actionable on its own.
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2.Information
- Examples:
- "The temperature in New York is 25°C."
- "Sales in March were $10,000."
- "The Fibonacci sequence starts with 0, 1, 1, 2, 3.“
-Role: Provides context and understanding but
does not enable decision-making.
- Storage: Stored in reports, dashboards, or
structured databases.
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3.Knowledge
-Definition: Knowledge is the meaningful
interpretation, synthesis, and application of
information to solve problems or make decisions.
- Characteristics:
- Information with understanding and insights.
- Answers "how" and "why" questions.
- Dynamic and actionable.
- Derived from experience, analysis, or
reasoning.
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3.Knowledge
- Examples:
- "The temperature in New York is ideal for
outdoor activities."
- "Sales increased by 20% in March due to a
successful marketing campaign."
- Role: Enables decision-making, problemsolving, and innovation.
- Storage: Stored in knowledge bases, ontologies,
or expert systems.
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4. Wisdom
- Definition: Wisdom is the highest level of the
DIKW pyramid, representing the ability to make
sound judgments and decisions based on
knowledge, experience, and ethical
considerations.
- Characteristics:
- Involves understanding the long-term
consequences of actions.
- Combines knowledge with values, ethics, and
intuition.
- Answers "what should be done" questions.
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4.Wisdom
- Examples:
- "Investing in sustainable practices will benefit
the company in the long run."
- "Prioritizing employee well-being leads to
higher productivity and retention."
- **Role:** Guides strategic decision-making and
long-term planning.
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Data , information , and Knowledge
Data Raw, unprocessed facts or figures.
"25," "New York," "98.6°F."
Information Data with context and meaning.
"The temperature in New York is 25°C."
Knowledge Information with understanding
and application.
The temperature in New York is ideal for outdoor
activities."
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Transformation Process
1. Data → Information:
- Data is processed, organized, and given
context.
- Example: "25°C" becomes "The temperature is
25°C."
2. Information → Knowledge:
- Information is analyzed, interpreted, and
applied to derive insights.
- Example: "The temperature is 25°C" becomes
"The temperature is ideal for plant growth."
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Transformation Process
3. Knowledge → Wisdom:
- Knowledge is combined with experience,
ethics, and intuition to make sound judgments.
- Example: "The temperature is ideal for plant
growth" becomes "Plant crops during this season
to maximize yield."
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Importance in AI and DecisionMaking
- Data: Provides the raw material for analysis and
machine learning.
- Information: Adds context and structure to data,
making it useful for analysis.
- Knowledge: Enables reasoning, problemsolving, and decision-making in expert systems
and AI.
- Wisdom: Guides long-term strategic decisions
and ethical considerations.
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Importance in AI and DecisionMaking
- Data is the raw material, information is data
with context, and knowledge is information with
understanding and application.
- Together, they form a hierarchy that transforms
raw facts into actionable insights, enabling
effective decision-making and problem-solving.
- In AI and expert systems, this hierarchy is
critical for building intelligent systems that can
reason, learn, and act.
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What is Knowledge Representation?
Knowledge Representation in AI describes
the representation of knowledge. Basically, it
is a study of how the beliefs, intentions, and
judgments of an intelligent agent can be
expressed suitably for automated reasoning.
 Knowledge representation in AI is not just
about storing data in a database, it allows a
machine to learn from that knowledge and
behave intelligently like a human being.

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Different Types of Knowledge
There are 5 types of Knowledge such as:
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Different Types of Knowledge
There are 5 types of Knowledge such as:
1-Declarative Knowledge – It includes concepts,
facts, and objects and expressed in a declarative
sentence.
2-Structural Knowledge – It is a basic problemsolving knowledge that describes the relationship
between concepts and objects.
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Different Types of Knowledge
3-Procedural Knowledge – This is responsible for
knowing how to do something and includes rules,
strategies, procedures, etc.
4-Meta Knowledge – Meta Knowledge defines
knowledge about other types of Knowledge.
5-Heuristic Knowledge – This represents some
expert knowledge in the field or subject.
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Cycle of Knowledge Representation
in AI
Artificial Intelligent Systems usually consist of
various components to display their intelligent
behavior. Some of these components include:
1) Perception

2)
3)
4)
5)
Learning
Knowledge Representation & Reasoning
Planning
Execution
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
The Perception component retrieves data or
information from the environment. with the
help of this component, you can retrieve data
from the environment, find out the source of
noises and check if the AI was damaged by
anything. Also, it defines how to respond when
any sense has been detected.
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
The Learning Component that learns from the
captured data by the perception component.
The goal is to build computers that can be
taught instead of programming them. Learning
focuses on the process of self- improvement. In
order to learn new things, the system requires
knowledge acquisition, inference, acquisition of
heuristics, faster searches, etc.
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The main component in the cycle is Knowledge
Representation and Reasoning which shows the
human- like intelligence in the machines.
 Knowledge
representation
is
all
about
understanding intelligence. Instead of trying to
understand or build brains from the bottom up, its
goal is to understand and build intelligent
behavior from the top--down and down and focus
on what an agent needs to know in order to
behave intelligently. Also, it defines how
automated reasoning procedures can make this
knowledge available as needed.

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The Planning and Execution components
depend on the analysis of knowledge
representation and reasoning.
 Here, planning includes giving an initial state,
finding their preconditions and effects, and a
sequence of actions to achieve a state in which
a particular goal holds. Now once the planning
is completed, the final stage is the execution of
the entire process.

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In the real world, knowledge plays a vital role
in intelligence as well as creating artificial
intelligence.
 It demonstrates the intelligent behavior in AI
agents or systems. It is possible for an agent or
system to act accurately on some input only
when it has the knowledge or experience about
the input.

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Alternative knowledge base
representations
Their are four types of knowledge representation
1. Semantic Networks
2. Frames
3. Logical representation
4. Production rule
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1.Semantic Networks
-Description: Semantic networks represent
knowledge as a graph, where nodes represent
concepts or entities, and edges represent
relationships between them.
-Structure: Nodes (e.g., "Bird," "Can Fly") and
edges (e.g., "is-a," "has-property").
- Use Case: Useful for modeling inheritance and
relationships in domains like biology, linguistics,
or knowledge graphs.
- Example: A semantic network for animals
might show that a "Penguin" is a "Bird" but cannot
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fly.
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
Advantages:
Semantic networks are a natural representation of
knowledge.
 Also, it conveys meaning in a transparent manner.
 These networks are simple and easy to understand.


Disadvantages:
Semantic networks take more computational time at
runtime.
 Also, these are inadequate as they do not have any
equivalent quantifiers.
 These networks are not intelligent and depend on the
creator of the system.

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-Description: Frames represent knowledge as a
collection of attributes (slots) and values (fillers)
for a given entity or concept.
-Structure: Each frame represents an object or
class, with slots describing its properties and
relationships.
-Use Case: Suitable for object-oriented domains
where entities have well-defined attributes, such
as engineering design or customer management.
- Example: A frame for a "Car" might include
slots like "Make," "Model," "Year," and "Color."
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
Advantages:
 It makes the programming easier by grouping the
related data.
 Frame representation is easy to understand and
visualize.
 It is very easy to add slots for new attributes and
relations.
 Also, it is easy to include default data and search for
missing values.
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
Disadvantages:
 In frame system inference, the mechanism cannot be
easily processed.
 The inference mechanism cannot be smoothly
proceeded by frame representation.
 It has a very generalized approach.
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Description:
Uses
formal
logic
(e.g.,
propositional logic, predicate logic) to represent
knowledge as logical statements.
Structure: Logical expressions, predicates, and
quantifiers.
Use Case: Suitable for domains requiring
rigorous reasoning and formal verification, such
as mathematics or software verification.
Example: A logic-based system might use
predicates like "Father(X, Y)" to represent family
relationships.
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
Advantages:
 Logical representation helps to perform logical
reasoning.
 This representation is the basis for the programming
languages.

Disadvantages:
 Logical representations have some restrictions and
are challenging to work with.
 This technique may not be very natural, and
inference may not be very efficient.
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Description: Extends traditional rule-based
systems to handle uncertainty by associating
rules with probabilities or confidence factors.
 Structure: If-then rules with weights or
probabilities.
 Use Case: Useful for domains where decisions
are based on uncertain or incomplete
information, such as financial forecasting or
fault diagnosis.
 Example: A rule like "IF fever THEN flu (0.7)"
indicates a 70% confidence in the diagnosis.

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
Advantages:
 The production rules are expressed in natural
language.
 The production rules are highly modular and can be
easily removed or modified.

Disadvantages:
 It does not exhibit any learning capabilities and does
not store the result of the problem for future uses.
 During the execution of the program, many rules
may be active. Thus, rule-based production systems
are inefficient.
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
The choice of knowledge representation
depends on factors such as:
a.
b.
c.
d.
Domain Complexity: Simple domains may use
rules, while complex domains may require
ontologies or graphs.
Uncertainty Handling:** Probabilistic or fuzzy
representations are better for uncertain domains.
Scalability: Graph-based or neural-symbolic
approaches are more scalable for large datasets.
Interpretability: Rule-based or logic-based
representations are more interpretable than neural
networks.
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
Determine whether the following statements are
true or false and correct the false statements:
Frames represent knowledge as a collection of attributes
images for a given entity or concept.
 Meta Knowledge defines knowledge about other types of
Knowledge.
 Knowledge is data that has been processed, organized, or
structured to provide context and meaning
 information refers to raw, unprocessed facts, figures, or
symbols that lack context or meaning.
 Semantic networks take a small computational time at
runtime
 In frame system inference, the mechanism can be easily
processed

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Complete the following statements:
 There are five types of Knowledge which
are:….,…,…,….
 There are four Knowledge Representation
Techniques which are:….,…,…,….
 Data refers to…..,…. figures, or symbols that
lack context or meaning.
 Representation Requirements are ….,….,….
 Information stored …,…..,….

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State two of the advantages and disadvantages
of the production rule and semantic network.
 State two of the advantages logical base
representation network

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