Exploring Knowledge Representation in AI

Aug 15, 2024

Notes on Knowledge Representation in AI

Introduction

  • Knowledge representation is essential for enabling computers to understand human language.
  • It involves a formal approach to represent knowledge for computer processing.
  • Aims to help computers reason, make choices, and resolve issues based on information.

Importance of Knowledge Representation in NLP

  • Natural Language Processing (NLP) heavily relies on knowledge representation to manipulate meaning in text data.
  • Key tasks in NLP include:
    • Information retrieval
    • Question answering
    • Text summarization
    • Sentiment analysis
    • Machine translation
  • NLP is a subfield of AI focused on human-computer interaction using natural language.

Career Opportunities in Knowledge Representation

  • Growing field with many exciting career paths:
    • AI Engineer
    • NLP Specialist
    • AI Researcher
    • Knowledge Engineer
    • Chatbot Developer
    • Data Scientist
  • Salaries:
    • Average AI Engineer salary in India: ₹9,80,000/year
    • Average AI Engineer salary in the US: $1,285/year

Agenda of the Lecture

  1. What is Knowledge Representation?
  2. Types of Knowledge in AI
  3. AI Knowledge Cycle
  4. Properties of Knowledge Representation
  5. Approaches to Knowledge Representation

Understanding Knowledge Representation with an Example

  • Example of organizing guest dietary restrictions in a table:
    • Each row = guest
    • Each column = name and dietary restriction.
  • Other examples:
    • Graphs for relationships between concepts
    • Decision trees for decision-making processes.

Types of Knowledge to Represent in AI

  1. Objects: Identifiable entities (e.g., car, person).
  2. Events: Specific occurrences (e.g., wedding, concert).
  3. Performance: Measures of task accomplishment.
  4. Meta Knowledge: Knowledge about knowledge (e.g., a car is a type of vehicle).
  5. Facts: True or false statements (e.g., "The sky is blue").
  6. Knowledge Base: Organized collection of knowledge (e.g., customer database).

Examples in a Car Dealership Domain

  • Object: Specific car model (e.g., Toyota Camry).
  • Event: Test drive or purchase.
  • Performance: Sales performance of a salesperson.
  • Meta Knowledge: Toyota Camry as a specific type of vehicle.
  • Fact: Camry's fuel efficiency rating.
  • Knowledge Base: Customer preferences, sales data, vehicle specs.

Types of Knowledge Representation

  1. Declarative Knowledge: Factual knowledge about the world.
  2. Procedural Knowledge: Knowledge about how to do things (skills and techniques).
  3. Meta Knowledge: Knowledge about other knowledge.
  4. Heuristic Knowledge: Knowledge from experience (trial and error).
  5. Structural Knowledge: Organization of information (how data is structured).

AI Knowledge Cycle

  1. Perception: Gathering information through senses.
  2. Learning: Acquiring new knowledge and modifying internal representation.
  3. Knowledge Representation & Reasoning: Creating models for reasoning and decision-making.
  4. Planning: Creating a sequence of actions to achieve goals.
  5. Execution: Implementing the plan based on internal representation.

Properties of Knowledge Representation

  1. Expressiveness: Ability to express a wide range of concepts and relationships.
  2. Inferential Adequacy: Support for reasoning with represented knowledge.
  3. Efficiency: Ability to manipulate and retrieve knowledge efficiently.
  4. Transparency: Understandable and modifiable by the user.
  5. Scalability: Ability to handle large amounts of data effectively.

Approaches to Knowledge Representation

  1. Simple Relational Knowledge: Organizing knowledge through relationships (e.g., familial relationships).
  2. Inheritable Knowledge: Knowledge passed from one entity to another (e.g., traits of animal classes).
  3. Inferential Knowledge: Knowledge inferred from other knowledge (e.g., medical diagnosis).
  4. Procedural Knowledge: Knowledge involving sequences of actions (e.g., steps to make tea).

Conclusion

  • Knowledge representation is critical in AI, helping to effectively organize and utilize knowledge for various applications.
  • Encouragement to share thoughts and subscribe for more content.