4.11 Approaches to knowledge representation
You can say what a representation is for, judge one against the four adequacy properties, and choose between relational, logical, procedural and inheritance-based knowledge.
Before:01. Mathematics for Machine Learning02. Python — Basics to Advanced
A representation exists to make inference cheap, not to store facts — one sentence that explains most design choices in this area. This topic judges any scheme against four adequacy properties: can it represent what is needed, infer from what it holds, do so efficiently, and acquire new knowledge. It opens the knowledge-representation run by giving the evaluation frame the next topics are scored against. The confusion to shed is treating representation as a storage question; databases store, representations reason.
Work through these
Explain what a representation is for: inference, not storage
A representation exists so that conclusions can be drawn, not so that facts can be stored. Holding that purpose in view is what makes the choices in this topic make sense.
Apply the four properties — representational adequacy, inferential adequacy, inferential efficiency and acquisitional efficiency
Four questions to ask of any representation: can it express what you need, can it derive what you need, can it derive it fast enough, and can the knowledge be acquired at all. The last one defeats more systems than the others.
Distinguish relational, logical, procedural and inheritance-based knowledge
Four broad families, from simple tables of facts, through formal logic, to knowledge embedded in procedures, and hierarchies that pass properties downward. Each family suits a different kind of knowledge.
Say what each approach is good at, and what it cannot do
Every representation buys some inferences cheaply and makes others impossible, and naming both sides is what an honest comparison requires. There is no general winner.
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