在現今知識經濟時代下，產業知識之擷取、儲存、管理與再利用為企業體保有產業競爭力之重要課題。然而，現今知識管理之相關技術多以文件知識的關鍵文字搜尋、版本控管、集中管理等重點進行發展，針對知識文件之內涵與結構解析之研究甚少，此乃造成知識真正有價值之資訊被忽略於知識管理課題之外，並使產業知識管理之有效性降低。此外，隨著網際網路技術進步，知識交換與分享於網路上進行已成為一種必然趨勢，如何有效解析並表達文件資訊使其易於閱讀與瞭解，成為企業進行知識管理另一項重要課題。因此，本研究乃根據網際網路下知識管理活動之特質，發展一套知識文件結構之解析模式，以文件中詞彙發生頻率及詞彙間關聯性為依歸，發展知識文件之詞彙截斷技術，以進行知識內容之剖析；並以詞彙截斷機制所得之斷詞組合為基礎，配合詞彙詞性分析模組，以決定斷詞組合之詞性結構。最後，再藉由RDF語法定義之知識本體結構解析，使知識文件產生具語意層次之結構，以有效表達知識文件之結構。除方法論與模式之發展外，本研究並完成一雛形系統開發與案例驗證，以確認方法論之可行性。本研究除了以既有文件庫為基礎，自動建置適用於各特定領域之詞頻庫與知識本體結構外，並融合詞彙發生頻率、詞彙關聯性與詞彙詞性等因子，使知識文件之表達結果具正確性與一致性，以便於知識文件之閱讀、交換與分享，進而提升產業知識管理效能與可再利用性，並強化企業知識管理之效度與深度。

In the knowledge-centric environment, enterprise knowledge acquisition, storage, management and reuse are the typical issues for enterprises to maintain their advantages in the global market. However, the present knowledge management techniques focus mainly on document search, version control and authorization. The contents and structure of documents that reveal the critical knowledge are rarely concerned. On the other hand, owing to the popularity of the Internet technology, more and more enterprise knowledge is exchanged and reused over Internet. In order to effectively explore the critical information in the free-from documents, a model for document structure analysis is developed in this research. In the proposed methodology, based on keyword frequency and correlation, document fragmentation and pattern analysis algorithms are utilized to analyze the document components and structure. Using the knowledge ontology defined based on the RDF syntax, the document components are then parsed into semantic structure. In addition to the document content analysis model, a prototype system is also developed and an IP management case is provided to verify the feasibility and effectiveness of the model. This research aims at developing an applicable approach to transform the free-form documents into structured semantic representation. As a result, the goal of automatic knowledge extraction and reused can be fulfilled and efficiency of enterprise knowledge management can be significantly improved.

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