現今的數位圖書館的檢索技術都是使用搜尋引擎或是資料庫的技術。其中以關鍵字比對(Keyword Matching)是最常用的方式。關鍵字比對的技術依靠語法上的比對而沒有語意上的比對，也就是使用者必須使用相同的字詞才能找到所需的資料。在此我們應用本體論(Ontology)所表達的知識來幫助系統了解在使用者查詢中所包含的語意，並且希望能夠在內容檢索時的召回率(Recall)及精準度(Precision)。這篇論文提出一個以在一個數位圖書館多代理人系統中本體論為基礎的內容檢索機制，並且提供較關鍵詞比對好的建所機制。我們為數位圖書館資訊服務設計了一個多代理人系統其中包括了：圖書館代理人，詞彙代理人，領域知識代理人，及資訊蒐集代理人。我們利用本體論來表示領域知識，在數位圖書館多代理人系統中的代理人利用本體論中所表達的知識來幫助系統了解使用者的查詢中的語意。經由這些代理人的合作，使用者用自然語言所表達的查詢可以轉換成查詢略圖(Query Schema)。根據這些被轉換出來的查詢略圖所提供的，代理人可以定義出的使用者查詢的語義和主題。根據這些被引導出來的資訊，資訊蒐集代理人可以從特定的資訊源取出資訊並且提供給使用者。
我們針對學校領域建立了一個實驗系統用來測試上述所提出的方法，並且以全文搜尋引擎為對照組來評估系統的效益。實驗的結果顯示這個系統在召回率與精準度上有較全文檢索引擎好的表現。

The thesis proposes a multi-agent system that could reason over domain ontology could help retrieve information better than keyword matching search engines. We build a multi-agent system for digital library (MADL) that consists of librarian agent, thesaurus agent, ontology agent, and information gathering agents to carry out the tasks. Through the cooperation among these agents and the utilization of lexical thesaurus and domain ontology, MADL can transform user’s simple structured natural language into query schemas, which are designed for domain and language specific. And with the derived query schema, Information Gathering Agents could extract information from a specific information source. Currently we have built an experimental system for a small set of questions in a school domain. We compare the result of search with MADL’s aid and the result of a pure full-text search engine. The comparison shows that the experiment system performs better recall and precision than a pure full-text search engine.

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