A knowledge base system (KBS) primarily consists of two core components: a knowledge base and an inference engine.
Knowledge Base
The knowledge base is the central repository that stores structured knowledge relevant to a specific domain. This knowledge can take various forms:
- Facts: Basic statements of truth about the domain. For example, "Humans are mammals."
- Rules: Conditional statements that define relationships and actions. For example, "IF a patient has a fever AND a cough, THEN they might have a cold." These are often expressed in an IF-THEN format.
- Concepts: Definitions and classifications of entities within the domain. For example, "A car is a vehicle with four wheels and an engine, used for transportation."
- Procedures: Step-by-step instructions on how to perform tasks or solve problems.
- Heuristics: Rules of thumb or best practices that are not always guaranteed but are often effective.
The data within a knowledge base can range from medical data, hardware specifications, company policies, to scientific facts. The way this knowledge is represented is crucial, and common knowledge representation methods include:
- Semantic Networks: Representing knowledge as nodes (concepts) and links (relationships).
- Frames: Representing knowledge as structured objects with attributes and values.
- Logic: Using formal logic to represent knowledge and reasoning.
Inference Engine
The inference engine is the "brain" of the KBS. Its primary function is to use the knowledge stored in the knowledge base to draw conclusions, make predictions, or solve problems. It does this by applying logical reasoning techniques to the knowledge. Key functions of the inference engine include:
- Reasoning: Applying rules and facts to derive new knowledge or answer queries.
- Searching: Exploring the knowledge base to find relevant information.
- Conflict Resolution: Managing situations where multiple rules apply or contradictory information exists.
- Explanation: Providing justifications for its conclusions, enhancing transparency and trust.
Common inference techniques employed by inference engines are:
- Forward Chaining: Starting with known facts and applying rules to infer new facts until a goal is reached.
- Backward Chaining: Starting with a goal and searching for evidence to support it.
- Rule-Based Reasoning: Using IF-THEN rules to deduce new knowledge.
In addition to these core components, some knowledge base systems may also include:
- User Interface: Allows users to interact with the KBS, ask questions, and receive answers.
- Knowledge Acquisition Module: Facilitates the process of adding, updating, and maintaining the knowledge in the knowledge base. This can involve manual input, automated learning from data, or a combination of both.
