在這篇論文中，我們提出一個領域本體論(Domain Ontology)為架構，結合潛在語意分析(Latent Semantic Analysis)技術的自動文件分類系統。在這個系統中，我們將已建構好的領域本體論其中的字詞集當作我們分類的基本元素，結合潛在語意分析擷取出文章語意的技術，建立領域本體的概念空間。此概念空間使用基因演算法(Genetic Algorithms)的方式分群，以此分群方式學習出文件在各類別的範圍。依據學習的結果，測試文件在新加入時便可根據文件範圍找到其適合的類別。在測試過程中，因為擷取出了此文章的概念，所以就算文章並沒有包括此關鍵詞，我們可以將此文章的概念與領域概念空間相對應，一樣的可以將此文章分到正確的類別。在實驗中，我們先請領域專家以人工方式定義了輕金屬的領域本體論關鍵詞集，以及對蒐集的文件資料分類，而以領域本體論為基礎的語意概念式自動文件分類系統，運用在中文文件方面其正確率可達69%，召回率可達50%；運用在英文文件方面準確率可達73%，召回率可達55%。比傳統的以關鍵字次數為分類方式正確率提升了19%，召回率提升了1%。

In this thesis, we propose an automatic concept-based document categorization system with Domain Ontology and take advantage of LSA as its technology. In this system, we prepare defined key-word sets which from Domain Ontology. The concept space is built with LSA. The vector in the concept-space is clustered with Genetic Algorithms. The boundaries of clusters are set up in the learning step. The test documents can be categorized correctly by the boundary. In the testing step, if a test document without key-word, we still can categorize them correctly. We use LSA to collect document real means, and map this document to the concept space. In the experiment, we invite the expert in light-metal domain. The expert defines light-metal Domain Ontology, and categorizes all documents. When Automatic Concept-based Document Categorization System with Domain Ontology Framework is used for Chinese documents, the average precision is 69% , and the average recall is 50%. While the system is used for English documents, the average precision is 73%, and average recall is 55%.

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　經濟部工業局金屬工業研究發展中心(2005)。輕金屬資訊網。線上檢索日期：2005年5月。網址：http://www.lightmetal.org.tw/
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