由於Ontology能夠描述特定知識領域內相關的概念與關係，利用此種特性，本論文提出一種基於Ontology架構的文件分類方式，以期能有效的協助知識管理者在電子文件的管理上達到正確的分類效果。本論文首先利用訓練方式，分別求得Ontology內的概念與關係之權重值，進而得到一個具有權重值的Weighted Ontology。當一份新聞文件需要分類時，則須先經由文件前處理程序得到新聞文件內的詞彙集合，然後與Ontology中各類別的Concept進行比對，以求得新聞文件與Ontology的對應關係。再來，我們將此對應關係轉換成具有權重的有向圖，並透過搜尋演算法找出有向圖中最長的路徑與權重值最大的路徑，再加上有向圖內各個節點所代表的對應Concept數量，則可產生藉以判斷文件類別的模糊變數，包括對應概念權重(MCW)、最大語意路徑長度(MSRPL)以及最大語意路徑權重(MSRPW)等三個模糊變數。最後，利用這三種模糊變數進行模糊推論，藉以評估該文件與各類別Ontology之間的相似度，達到正確的分類結果。經由實驗証實，本論文所提出之web Services能有效地進行中文文件自動分類。

　　Because of the Ontology can describe the specific domain knowledge with the concepts and relationships, we utilize the properties of the Ontology to assist in classifying the documents automatically. In this thesis, we first compute the weights of concepts and relationships based on the tf*idf values, respectively, then construct the weighted Ontology based on the weights. When a news is necessary to be classified, the document pre-processing procedure will generate a term set of the news, then the mapping relationship between the concept of the Ontology and the term set of the news will be produced by a mapping mechanism. In addition, the mapping relationship will be transformed into a weighted digraph. By a search algorithm, we can find the maximal path length, the maximal weights of all the paths, and the number of the concepts in the digraph for generating the fuzzy variables of the Fuzzy Inference Mechanism(FIM). The three fuzzy variables including Mapped Concept Weight (MCW), Maximal Semantic Relation Path Length (MSRPL), and Maximal Semantic Relation Path Weight (MSRPW) are utilized in FIM to evaluate the similarity between the news and the domain Ontology. The experimental results show that our approach can effectively classify the Chinese documents correctly.

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