| 研究生: |
李韋承 Li, Wei-Cheng |
|---|---|
| 論文名稱: |
建構階層式知識地圖及其知識搜尋法之研究 Toward An Ontology-based Hierarchical Knowledge Map and its Effective Knowledge Search Approach |
| 指導教授: |
李昇暾
Li, Sheng-Tun |
| 學位類別: |
碩士 Master |
| 系所名稱: |
管理學院 - 資訊管理研究所 Institute of Information Management |
| 論文出版年: | 2005 |
| 畢業學年度: | 93 |
| 語文別: | 中文 |
| 論文頁數: | 64 |
| 中文關鍵詞: | 自我組織映射圖類神經網路 、資訊擷取 、知識地圖 、本體論 、知識管理 、文件視覺化 |
| 外文關鍵詞: | Information retrieval, Knowledge map, Self-organizing map, Document visualization |
| 相關次數: | 點閱:108 下載:3 |
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自網際網路(World Wide Web)發明以來,全球資訊量便以指數型態爆炸性的成長,而身處於現在的知識社會,每個人對於資訊的需求也與日俱增;但是,如此龐大的資訊卻又造成了資訊過載,使得傳統的資訊檢索工具已無法因應知識工作者所需,是以如何能夠在一片茫茫的資訊洪流中,找尋相關且有用的資訊,便成為知識管理的一個重要的課題,也因而一套更有效的資訊瀏覽工具是有其必要性。在新進發展的各種瀏覽工具中,知識地圖利用了電腦的強大運算功能,臨摹地理學上製圖的方式,彙整大量的資料,整理出有用的資訊,以視覺化的地圖方式,有系統地表達出文件分布概況,以提供整體性的瀏覽,進而轉換成知識;而樹狀階層式的表達方式,則有助於使用者概觀地了解此份知識文件的結構,還可以摺疊冗贅的知識地圖,或是將粗略的知識地圖精緻化。本計劃將提出一個新的建置階層式知識地圖的方法論,其利用本體論主導文件特徵擷取,再根據文件彼此間的相似度,輔以非監督式階層式自我組織映射圖類神經演算法,配合視覺化之圖形工具,分析知識文件所隱含的語意架構,建構出適合知識工作者瀏覽之階層式知識地圖,最後再以客觀的衡量標準,以驗證本研究所提出的瀏覽模式工具之實用性與效度。
Since the emergence of the Internet, the amount of information has been grown exponentially. Everyone, as a member of knowledge society, is eager for the assistance and advantage brought by information. However, such a huge amount of information not only results in information overloading but also makes traditional information-retrieval tools incapable of dealing with this situation effectively. Thus, a more intelligent information searching and browsing methodology becomes the key issue in digging out useful knowledge in the so-called information smoke. In particular, knowledge map has been shown as a successful tool for tackling the issue. Knowledge maps can take the advantage of computer strength in powerful computation ability, imitate drafting techniques of geography field, compile innumerous data and useful information and then demonstrate the implicit relationship existing the knowledge objects in a visual map. In addition, it provides a whole picture for knowledge works when browsing so that they can benefit from the knowledge sharing and distribution. Moreover, a tree-like hierarchy can help them understand how the architecture of knowledge documents is set instantly in a general view point or how superfluous knowledge maps can be folded up. In this project, we will propose a new methodology of constructing a hierarchical knowledge map which mainly involves ontology-guided feature extraction, conceptual relationship construction by the hierarchical growing self-organizing map algorithm, visualization of the implicit semantic relationships The effectiveness and efficiency the proposed methodology will be justified by performing experiments on real-world applications.
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