臺灣博碩士論文加值系統

本論文研究利用物件的屬性來作query expansion。 其步驟是將使用者query所訂定的屬性與物件的屬性逐一比對。然後將合乎條件的物件名稱作為檢索的關鍵字。這樣的query expansion作法，可有效將使用者的概念與數位圖書館/博物館所包含的詞彙作連接，以使得檢索的recall rate及precision rate均得到提昇。本論文詳述整個query expansion過程中所使用到的各種資料結構，及這種query expansion所能達到的效果。 最後本論文亦以一個實際的例子探討所提出的query expansion機制所達到的precision rate與 recall rate.

The thesis proposes a query expansion mechanism on the attributes of the objects
in a digital library. The query expansion mechanism involves matching the desired
attributes specified in the user's query and the attributes of the objects. Objects with
matched attributes are then forwarded to the search utilities. The proposed query
expansion mechanism effectively closes up the gap between the user's concept and
terms presented in the digital library. As a result, the precision and recall rates of the
search utilities are improved. This thesis elaborates the data structures developed for
conducting the proposed query expansion and demonstrates the effects achieved. This
thesis also conducts an experiment to evaluate the proposed mechanism.

第一章 緒論1
1.1 資訊搜尋與查詢擴展(Information Retrieve and Query Expansion)1
1-2 論文章節概述4
第二章 相關研究5
2-1 向量空間為基礎的Query Expansion6
2-2 以知識庫為基礎的Query Expansion10
2-2-1 Knowledge-Bases11
2-2-2 Search Strategies13
2-2-3 Knowledge-Base Combination Strategies14
2-2-4 一些討論16
第三章 本論文Query Expansion之方法設計19
3-1 物件屬性詞典的架構19
3-2 屬性表20
3-3 同義詞集24
3-4 別名25
3-5 本論文的實作方法26
3-5-1 調整Query內的字串26
3-5-2 取得需要的屬性節點26
3-5-3 取得相關的詞集36
3-5-4 由詞集取得相關的文章資訊37
3-6 小結38
第四章 效能評估41
4-1 系統開發環境41
4-2 系統效能評析41
4-2-1 precision 及 recall 的效能評析41
4-2-2 時間的效能評析42
第五章 結論與未來展望44
5-1 結論44
5-2 未來的研究方向45
實驗結果46
參考文獻48

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