AI Knowledge Representation

What is Knowledge Representation in AI?

Knowledge Representation refers to the process of encoding information about the world into a format that an AI system can understand and use to perform reasoning, decision-making or problem-solving tasks. It bridges the gap between human understanding and machine computation.

For example:

Human Knowledge: “A dog is a mammal and has four legs.”

Machine Representation: Facts and rules like:

isMammal(Dog).
hasLegs(Dog, 4).

Why is Knowledge Representation Important?

1. Enables Reasoning: AI systems use stored knowledge to deduce new facts and make decisions.
2. Improves Communication: KR allows humans and AI to interact effectively using shared data models.
3. Facilitates Learning: Systems can expand their knowledge base by learning new facts.
4. Supports Problem-Solving: Representation simplifies complex problems for efficient solutions.

Types of Knowledge in AI

1. Declarative Knowledge: Facts and information about the world.
   • Example: “The sky is blue.”
2. Procedural Knowledge: Instructions or methods for performing tasks.
   • Example: “Steps to solve a Rubik’s Cube.”
3. Semantic Knowledge: General knowledge stored as concepts and relationships.
   • Example: “A car is a vehicle.”
4. Episodic Knowledge: Information tied to specific events or experiences.
   • Example: “I attended a meeting at 10 AM.”

Types of Knowledge Representation

1. Logical Representation

Uses formal logic to represent facts and rules about the world.

Example:

All humans are mortal.
Socrates is a human.
Therefore, Socrates is mortal.

In Predicate Logic:

∀x (Human(x) → Mortal(x))
Human(Socrates)
⇒ Mortal(Socrates)

2. Semantic Networks

Represents knowledge as a graph with nodes (concepts) and edges (relationships).

Example:

• Node: “Dog”Edge: “is a” → Animal

Graph Representation:

Dog → is a → Animal
Dog → has → Four Legs

3. Frame Representation

Organizes knowledge into structures called frames, representing objects and their attributes.

Example:

Frame: Dog
Attributes: {Species: Mammal, Legs: 4, Sound: Bark}

4. Production Rules

Represents knowledge as IF-THEN rules.

Example:

IF it rains
THEN take an umbrella.

5. Ontologies

Represents knowledge as a hierarchy of concepts and their relationships.

Widely used in semantic web technologies.

Example:

Car → is a → Vehicle
Vehicle → is a → Transport

Challenges in Knowledge Representation

1. Complexity: Representing all aspects of human knowledge is difficult.
2. Ambiguity: Information can have multiple interpretations.
3. Incomplete Data: AI systems must handle incomplete or uncertain knowledge.
4. Scalability: Large knowledge bases can become unwieldy.

Example: Knowledge Representation in Python

Let’s create a simple representation using a semantic network.

class KnowledgeGraph:
    def __init__(self):
        self.graph = {}

    def add_relationship(self, subject, relationship, obj):
        if subject not in self.graph:
            self.graph[subject] = []
        self.graph[subject].append((relationship, obj))

    def get_relationships(self, subject):
        return self.graph.get(subject, [])

# Create a knowledge graph
kg = KnowledgeGraph()
kg.add_relationship("Dog", "is a", "Animal")
kg.add_relationship("Dog", "has", "Four Legs")
kg.add_relationship("Animal", "can", "Breathe")

# Query relationships
print("Dog Relationships:", kg.get_relationships("Dog"))

Output:

Dog Relationships: [('is a', 'Animal'), ('has', 'Four Legs')]

Applications of Knowledge Representation

1. Natural Language Processing (NLP):
   • Parsing and understanding human language.
   • Example: Virtual assistants like Siri and Alexa.
2. Expert Systems:
   • Solving domain-specific problems using stored knowledge.
   • Example: Medical diagnosis systems.
3. Robotics:
   • Representing the environment for navigation and interaction.
4. Search Engines:
   • Understanding and retrieving relevant information.
5. Semantic Web:
   • Enhancing the web with structured and machine-readable data.

Advantages of Effective Knowledge Representation

• Enhances reasoning capabilities.
• Enables efficient problem-solving.
• Improves system scalability.
• Facilitates machine learning integration.

Key Takeaways

1. Knowledge Representation is essential for AI to reason and act intelligently.
2. Logical, semantic, frame and production rule representations each serve specific needs.
3. Real-world applications, from expert systems to robotics, rely heavily on KR.

By mastering knowledge representation, students can build smarter, more efficient AI systems.