電腦科技及網路技術的發展導致資訊大量的產生，愈來愈多的專利知識文件也增加分類一致性與迅速性的難度。甚至連文件搜尋的快速與準確性也更加困難。為了解決這個問題，許多專家視文件管理，特別是專利知識文件，為一個重要的議題並投入專利領域研究中。在傳統的分類方法中，領域專家審視文件內容後依據他們的經驗來分類文件類別。由於傳統的方法常常導致分類結果不一致，自動文件分類變成一個重要的研究。本研究提出一個以預先建置的本體論架構為基的類神經網路的專利文件分類系統。首先，該系統可藉由使用詞性及句型分析來擷取文件中的特徵值。接下來系統將比對這些特徵值與本體論架構中的概念(Concept)及關係(Relation)，並利用本文提出的詞彙比對方法(Terminology mapping method)來考慮來自概念與關係的影響，並轉換成類神經網路模式的輸入值。最後，系統利用一訓練完整的類神經網路模式來計算並推論文件與類別的相關性。此研究以Chemical mechanical polishing (CMP) 專利知識文件與專利商務知識新聞兩個領域做為系統的載具,並測試系統的效能。CMP是一種廣泛應用在半導體製程上的平坦化技術，因此其專利的發展與應用極其重要。專利商務知識則是有關專利的開發、交易及應用等相關新聞。做好上述兩個領域文件的分類，不但有助於文件搜尋的速度及準確性，更進一步也能幫助我們迴避專利陷阱。在本研究提出的系統中，我們利用國際專利分類碼(IPC)作為專利分類的標準，並且進行系統效能的評估。

The development of modern computer science leads to information being generated speedily. More knowledge documents about patent are difficult to be classified consistently and promptly. In order to solve this problem, many specialists take document management, especially for technical reports and patent documents, as a significant research issue combining the expertise of IT, IP, and domain experts. In traditional categorization for patent documents, domain experts classify documents based on their experiences after reviewing documental contents. The development of automatic document categorization becomes an important research because traditional methods often bring inconsistent classification results. In this thesis, a categorization method using artificial neural network (ANN) is developed to classify patent documents based on pre-constructed ontology. Firstly, this system extracts the features of a document by using morphological analysis and sentence analysis. Secondly, these features are matched with classes and relations of pre-constructed ontology, and transferred into the inputs of ANN using two weight-transferring functions proposed in this research. Thirdly, a well-trained ANN model is applied to calculate and infer the relationships between given document and categories. We take two domains, Chemical Mechanical Polishing (CMP) and business knowledge documents, as our case studies to demonstrate the proposed system at work. International Patent Classification (IPC) is constructed as the classification schema and hierarchy.

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