7-1 結論
本論文針對社交標籤資源提出一個探勘前k名擴展查詢字集的演算法。本方 法針對使用者所給予的查詢標籤找出包含查詢字的物件，分析這些物件中所包含 的標籤字，透過 UT-tree 樹狀結構的儲存，在探勘可用性前 k 名擴展查詢字集時，
能夠避免產生不必要的擴展查詢字集。最後結合可用性值上限值及下限值的概念，
讓 UT-tree 能夠動態建立，不用將資料物件全部建立完之後，才進行探勘。
另外，本論文使用兩種挑選代表標籤的方法，針對物件中的標籤集合進行過 濾，保留與查詢字關聯程度高的標籤。根據實驗結果證實，使用代表標籤探勘擴 展查詢字集的效果明顯優於未使用代表標籤。本論文也評估所提出的 UT-growth 與 Dynamic UT-growth 演算法與相關研究方法的效率，從實驗數據中，Dynamic
UT-growth 的執行效率優於另外兩個演算法。此外，透過調整不同參數模擬資料 分佈的密集性與稀疏性，觀察對於擴展查詢字集大小的影響，其實驗結果符合不 同的資料分佈能夠結合出的擴展查詢字集大小。
7-2 未來研究方向
本論文所使用的標籤資源，除了 Delicious 有使用者對於連結的儲存次數，其 他都是用隨機方式給予資料物件的可用性分數值，這對於在計算擴展標籤字集的 分數值會有失真實性，未來可以針對這部分提供一個客觀的評分模式，處理沒有 物件可用性分數值的標籤資源。
由於在重覆率實驗的效果並不是很好，在這部分可以加入判斷機制，能夠使 得擴展查詢字集涵蓋到不重覆的物件，例如：考慮以挑選的擴展查詢字集所涵蓋 的物件，比較彼此間交集的物件，若交集達一定個數則不考慮。上述方法去能有 效降低重覆率，但卻會額外增加比較時間降低效率，所以將來可以再針對這部分 更進一步進行研究。
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