在面對現在資訊爆炸的時代，搜尋引擎變成每個人生活中不可或缺的工具，因此如何協助使用者過濾過量的資訊，同時考量個人搜尋意圖，達成個人化的搜尋排序一直是相當重要的議題。 基於上述的理念，本研究以知識本體描繪使用者偏好的框架為藍圖，提出在中文環境下的關鍵詞推薦系統，實現中文環境下的查詢擴展。透過網頁爬行器分析使用者過去瀏覽的所有網站地圖，以正規概念分析法自動建構涵蓋面較廣的個人化領域知識，同時以「知網」為輔，結合查詢擴展的方法與個人化知識本體的自動學習，檢索到更為完備的資訊。當使用者輸入關鍵字時，系統會比對關鍵字與使用者檔案中的個人化知識本體中的概念，產生與關鍵字概念相仿的延伸關鍵字集合推薦給使用者，藉以擷取更多描述同一概念的文件資訊。根據實驗結果顯示，本系統有效的提升了七成以上的檢索精確率，最佳的效能提升了兩倍，證明藉由過濾大部分與使用者興趣不相關的網頁，以取得使用者真正想要的資訊，相較於傳統的本體論查詢擴展方法，本研究提出的演算法利用使用者知識庫的自動產生、涵蓋面寬廣的訓練資料來源擷取、半自動的中文化擴展字詞推薦與適用於繁體漢字的知網義原庫，的確能有效提升在中文環境下資訊檢索的精確率。 Search engine has become an essential tool in the era of the information explosion, hence the topic of helping users to filter an excess of information and take personal implicit searching intentions into consideration in order to reach personalized searching ranking has always been important. Knowledge ontology was used to depict user’s preference and a Chinese keyword recommendation system was proposed to accomplish a Chinese Query Expansion. Analyzing the site maps of the whole user’s past browsing via web crawler, constructing a wider range of personalized domain knowledge automatically by Formal Concept Analysis, and combining Query Expansion and personal ontology which is automatic-learning through HowNet, the more complete information can be accessed easily. When user submits keywords, the system will compare keywords and concepts of personalized ontology in user’s profile in order to produce extended keyword sets similar to the keywords inputted and to be recommended to user to acquire more document information including the same concepts. The experimental results show that the system increases the retrieval precision over 70% and the retrieval precision almost doubles. By filtering most web documents unconcerned with user’s interests to acquire the actual needed information. The algorithm we proposed that provide automatic-generated user’s knowledge database, a wider range of training data source, a semi-automatic recommended mechanism of Chinese expansion words, and a sememe database of HowNet in Traditional Chinese, is proved to have better retrieval accuracy in the Chinese environment compare to methods of ordinary ontology query expansion.
