資訊技術進步以及網際網路發達，人們的生活從以前單元性演變成現在的多元化，網際網路更是提供更多學習的資源，所以知識管理在資訊網路層面更是重要。資料及資訊，彼此之間存在著更深層的關聯性、內隱知識等，無法初步被清楚定義出來。因此需要一個合適的知識管理平台來輔佐，其中知識本體可以提供數位資訊需要制式化且具備讓機器可理解、傳遞的知識結構、幫助知識管理。
但也因為科技的快速進步，許多資訊都以電子文件的方式呈現，資訊累積的速度相當快及龐大。資訊量的快速成長卻使得在搜尋相關的資訊時，一般人通常只能透過關鍵字查詢的方式，但使用者仍需面對著大量的搜尋結果，往往須經過長時間的篩選才能得到真正想要的資訊。為了對文件進行分門別類的加值處理，使得文件易於管理、利用，透過分類可以讓我們提升學習及表現的效率，文件分類在處理和組織大量的新聞資料中扮演著一個極為重要的角色。然而現在的文件資料多會存在著階層式關係，對此關係的分類處理方法一般都只是用壓平式(Flat)，因此會忽略掉與父子(Parent-Child)及兄弟(Sibling)之間關聯的因子。
對應到公司的運作和管理上，公司也會採取電子文件的方式，透過網際網路定期的發布公司訊息與產品資訊上，但也因為電子化文件成長快速，在不斷的更新各項公司資訊與產品發表的內容之虞，一般多以人工瀏覽文件再加以分類的方式，不但耗時耗力且耗費成本，造成文件並未有效管理與利用，這樣的潛在問題一直存在著。所以本研究將以資訊檢索、階層式分類及知識管理的概念，套用在N公司的營運。本研究將N公司產品新聞進行階層式分類，研究發現產品新聞標題及本文中，各以30%及70%的權重方式，並以SVM支援向量機的分類方法，可以達到最佳的分類效果。最後結合主題地圖，將主題與主題之間的關聯串接起來，藉此能夠有效提供使用者在資訊搜尋中獲取更完整的參考資料，發揮產品的知識價值，提供一個更寬廣的檢索視野。

Currently, knowledge management for Internet is a new trend. Although classification can filter noisy information, it is not easy to define the relationship between data sets and to extract tacit knowledge contained in data. Therefore, constructing a platform of knowledge management for providing realizable information should be considered. For achieving the goal of this thesis, topic map is used for knowledge management.
With the advance of information technology and Internet, many documents are presented in digital formats. Search engines are designed for users to use keywords to search documents. Because the searching process may spend much time to filter noisy information from abundant results. Therefore, an efficient classification mechanism should be implemented in processing and organizing the abundant information. Classification can categorize and systematize electronic documents.
Some enterprises regard Internet as a good way to broadcast product information and news. But the processing of classification still is by manual and thus fails to provide knowledge relationship for knowledge information. This study will adapt the concept of information retrieval, classification and knowledge management to build a hierarchical classification via Support Vector Machines (SVM) technology and topic map concept.

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