隨著網際網路的普及化，可以獲得的資訊越來越多元化，然而也伴隨著資訊過量的問題產生。因此如何在網際網路上快速且正確的取得所需的資訊，已經成為一個重要的議題。而如何協助使用者快速獲得真正需要的資訊正是文件分群系統的目的，也是本研究的動機。
詞彙鏈是常用於分析文件的方法，其藉由辭典建立，但準確率不是十分理想，本研究認為這個缺點可使用特定領域的知識本體論來彌補。因此，本論文提出”結合本體論與詞彙鏈群聚”的文件分群方法。使用本體論進行候選詞彙挑選以及詞彙鏈建立，以詞彙鏈分割演算法進行詞彙鏈群聚。透過詞彙鏈群聚計算鏈結密度向量進行文件分群，並使用鏈結密度向量衡量文件之間的相似度值，提供使用者瀏覽所需文件與相近的文件。
根據實驗的分析，證實使用本體論與詞彙鏈群聚能有效的提升文件分群的準確率。使用詞彙鏈群聚的分群方式與K-means、Bisecting k-means演算法比較也有顯著的差異。

With the popularity of the World Wide Web, more and more information is accessible on the Internet. The explosion of information leads to the information overload problem. How to find out users’ interested information efficiently and accurately has become an urgent issue. Document clustering is a direction of retrieving information effectively.
Lexical chain that created by dictionary is a method in text data analysis. The defect of lexical chain is the low precision. This thesis proposes a document clustering method combining ontology and lexical chain clusters to compensate the disadvantage of only using lexical chain. First we utilize the domain ontology to extract candidate words and express documents with lexical chains. The lexical chains are clustered based on a lexical chain division algorithm, where each document is assigned to one cluster by computing the similarity between the document and chain density vectors. Finally, we can effectively and efficiently cluster documents.
Compare with the k-means and bisecting k-means algorithms, the experimental results reveal that the proposed method can improve significantly the precision of document clustering.

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