Difficulty: Easy
Correct Answer: All of the above
Explanation:
Introduction / Context:
Knowledge-based systems and expert systems depend on how knowledge is represented and stored. A flexible Knowledge Engineering System (KES) often supports multiple representation paradigms to capture facts, rules, relationships, and even unstructured information useful for reasoning or retrieval.
Given Data / Assumptions:
Concept / Approach:
Typical KES platforms allow associations (semantic links between entities), procedural or rule-based actions (if–then rules, forward/backward chaining), structured schema (frames, classes, slots), and sometimes free text to store definitions, explanations, or case descriptions leveraged by information retrieval or case-based reasoning. Real-world systems blend these to create rich knowledge bases that are both searchable and actionable.
Step-by-Step Solution:
1) Examine typical knowledge representations: rules, frames, semantic networks, cases.2) Map these to options: associations (semantic network), actions (rules/procedures), schema (frames/classes), free text (cases/notes).3) Conclude that a capable KES can store all of them to support varied reasoning tasks.4) Choose the comprehensive option.
Verification / Alternative check:
Review examples like frame-based shells (e.g., LOOM, KL-ONE descendants), rule engines, and case-based systems; hybrid platforms routinely mix structures and text annotations to improve explanation facilities.
Why Other Options Are Wrong:
Common Pitfalls:
Assuming a knowledge base must be purely rule-based; overlooking the role of unstructured text for explanations and justifications.
Final Answer:
All of the above
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