Knowledge Representation and Reasoning

UNIT – 3

1-Mark Questions

1. What is knowledge representation in AI?

Answer: It is the process of encoding knowledge about the world into a form that a computer system can utilize
to solve complex tasks.

2. Define knowledge engineering.

Answer: Knowledge engineering is the process of designing and building intelligent systems by acquiring and
structuring knowledge from domain experts.

3. What is a frame in AI?

Answer: A frame is a data structure used to represent a stereotyped situation, consisting of slots (attributes) and
values.

4. Mention one use of rules in knowledge representation.

Answer: Rules are used for reasoning and decision-making in expert systems using IF-THEN logic.

5. What is an object-oriented system?

Answer: An object-oriented system represents knowledge using classes, objects, methods, and inheritance.

6. Define semantics in natural language processing.

Answer: Semantics is the study of meaning in language and how it is interpreted by AI systems.

7. What is the purpose of slots in frames?

Answer: Slots in frames hold values or pointers to other frames, representing the properties of the object.
8. Give one example of a production rule.

Answer: Example: IF it is raining THEN take an umbrella.

9. Name a level of representation in AI.

Answer: Semantic level.

10. What is inheritance in object-oriented representation?

Answer: Inheritance allows a subclass to inherit attributes and behaviors from a parent class.

10 Five-Mark Questions

Q1. Explain the role of knowledge engineering in AI.

Answer:
Knowledge engineering involves the process of designing systems that simulate expert human reasoning. It
includes knowledge acquisition, modeling, and encoding into machine-processable formats.

Steps:

 Identifying domain knowledge.

 Structuring it into logical formats.
 Creating ontologies or rule bases.
 Testing and validating reasoning.

📌 Diagram: Knowledge Engineering Lifecycle

Q2. What are frames? Explain their structure with an example.

Answer:
A frame is a data structure for representing a stereotyped situation. It consists of slots (attributes) and fillers
(values or rules).

Example: Frame: Car

 Slot: Type → Sedan

 Slot: Color → Red
 Slot: Engine → {Type: Petrol, Capacity: 1.5L}

📌 Diagram: Frame Structure

Q3. Differentiate between rules and data in knowledge representation.

Answer:

 Data: Facts, objects, and instances. E.g., “John is a doctor.”

 Rules: Logical inferences like IF-THEN statements.
Example:
IF John is a doctor THEN John can prescribe medicine.

📌 Diagram: Rules vs Data

Q4. Explain the basic structure of object-oriented knowledge representation.

Answer:
Object-oriented representation structures knowledge using objects, classes, and inheritance.

 Object: Specific instance with data and behavior.

 Class: Blueprint for creating objects.
 Inheritance: Objects inherit properties from their class.

📌 Diagram: Object-Oriented Hierarchy

Q5. Describe any four levels of knowledge representation.

Answer:

1. Lexical Level – Words and their meanings.

2. Syntactic Level – Grammar rules.
3. Semantic Level – Meaning of sentences.
4. Pragmatic Level – Contextual use of language.

📌 Diagram: Levels of Representation

Q6. Compare semantic networks and frames.

Answer:

Feature Semantic Network Frames

Structure Graph (Nodes + Object-like
Edges)
Representation Relationships Attributes (Slots)
Best for General relationships Structured objects

📌 Diagram: Semantic Network vs Frame

Q7. Describe natural language semantics and its role in AI.

Answer:
Semantics deals with understanding the meaning of natural language. In AI, it’s used for:

 Information retrieval
 Machine translation
  Chatbots

Example:
Sentence: “Ram ate an apple.”
Semantic Role: Ram = Agent, Apple = Object

📌 Diagram: Semantic Role Labelling

Q8. What are production rules? Explain with an example.

Answer:
Production rules are IF-THEN logic structures used for decision-making.

Example: IF temperature > 100°C THEN trigger alarm.

📌 Diagram: Rule-Based System

Q9. Explain the concept of inheritance in object-oriented systems.

Answer:
Inheritance allows subclasses to acquire properties of parent classes.

Example:
Class Animal → Legs = 4
Class Dog inherits from Animal → Legs = 4
📌 Diagram: Inheritance Tree

Q10. Explain the advantages of using frames in AI.

Answer:

 Organizes complex data.

 Supports default reasoning.
 Encourages reuse via inheritance.
 Combines procedural and declarative knowledge.

📌 Diagram: Frame with Default Values and Rules

10 -Mark Questions
Q1. Describe in detail the process of knowledge engineering in expert system design.

Answer: Knowledge engineering involves:

1. Knowledge acquisition – Gathering facts from experts.

2. Conceptualization – Organizing into schemas or ontologies.
3. Formalization – Encoding using logical languages.
4. Implementation – Coding into systems (rules, frames, etc.).
5. Validation – Testing correctness and usefulness.

📌 Diagram: Expert System and KE Process

Q2. Explain different types of knowledge representation techniques with examples.

Answer:

1. Semantic Networks – Represent relationships as graphs.

2. Frames – Represent objects with attributes and values.
3. Rules – Encode logic via IF-THEN rules.
4. Logic-based – Uses propositional or predicate logic.
5. Object-Oriented – Based on classes and inheritance.

📌 Diagram: Knowledge Representation Techniques

Q3. Discuss object-oriented systems in AI and compare with rule-based systems.

Answer: Object-Oriented:

 Uses classes, objects, and inheritance.

 Modular, reusable. Rule-Based:
 Uses production rules (IF-THEN).
  Good for decision-making.

📌 Diagram: Object-Oriented vs Rule-Based

Q4. How does natural language semantics contribute to intelligent systems? Explain with examples.

Answer: Semantics helps AI understand, interpret, and generate meaningful sentences.

 Machine Translation: “Hola” → “Hello”

 Chatbots: Interpret intent.
 Voice Assistants: Map spoken commands to actions.

📌 Diagram: NLP Semantics Pipeline

Q5. Explain the significance of levels of knowledge representation in AI.

Answer:

 Lexical: Words (dictionary meaning).

 Syntactic: Sentence structure.
 Semantic: Actual meaning.
 Pragmatic: Context and intent.

All levels work together for tasks like question answering, machine translation, and dialogue systems.

📌 Diagram: NLP Levels Pyramid