現今知識已成為企業最珍貴的資產，如何運用相關資訊科技保存企業的知識資產，將是企業取得競爭優勢之關鍵。如何把知識儲存起來、以便往後重覆利用，將是現階段重要的研究課題之一。在知識管理與推理的相關研究，因領域的不同而有不同的方法。適用於不易建構條例規則且有多筆案例相關資料的案例式推理(Case-Based Reasoning)則是一例。
　　一般的案例式推理，往往針對欲解決的問題領域提出多個特徵屬性，做為案例擷取之用。學者們普遍採用最近相鄰法(k nearest neighbour algorithm, KNN)，藉由各個特徵屬性根據最近相鄰法來計算出各個舊有案例與問題案例的相似程度。但這些特徵屬性彼此間的關係卻都視為同一階層、同等關係，甚至是相同影響程度。雖然KNN亦加入權重來區別各個特徵屬性所影響的程度，唯本研究認為各個特徵屬性彼此間可能有從屬或階層的關係。若能將屬性之間的從屬與階層關係透過權重計算來表現出來，將有助於增加擷取相似案例的正確性。
　　此外針對案例擷取的相似度計算方面，本研究提出一浮動階層式權重計算的模式，此模式將藉由將案例庫內的案例以欲解決之問題分為數個群組，各個群組擁有各自不同的浮動權重表。當新的問題案例來臨時，將問題案例直接與案例庫的各個案例進行相似度計算，其中部份屬性的權重將因案例所屬之案例群組不同，而得到不同的權重值。藉由浮動階層式權重計算，可望擷取出更適合的舊有案例，進而得到更佳的解決方式。

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中文部份
林建昇，「以知識圖呈現知識之實證研究」， 國立中正大學資訊管理研究所碩士論文，民國89年

英文部份
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