傳統的搜尋引擎的搜尋方式皆以單一網頁為單位作索引，因此無法記錄網頁之間的順序結構關係；另外，傳統搜尋引擎對於自然語言式的問句仍然沒有較為有效的處理方式。針對上述兩個問題，首先，本論文先提出一個結構化索引方法來記錄網頁之間的連結結構關係（link structure），解決以往以單一網頁為索引單位無法記錄網頁間結構資訊的問題；此外，對於自然語言式問句的處理方式，本論文將提出一個Tri-link對應模型（TMM）來找出問句中隱含的結構，並利用這樣的結構去對應出網站之Tri-link結構，計算問句與Tri-link結構之間對應的機率數值；本論文中另外還利用啟發式（heuristic）之方法將問句平分成三等份當成問句結構，並利用「與位置相依之相似度 （PDS）」與「與位置獨立之相似度 （PIS）」兩種不同的方式計算問句結構與Tri-link之相似度，這兩種方式是以傳統向量空間模型（Vector Space Model）為基礎所變化而來。本論文之實驗方面，將比較使用者之問句對於結構化索引方式與非結構化索引方式之優劣，經由實驗結果，我們發現對於自然語言式問句來說，結構化索引方式之搜尋正確率將會高於非結構化索引的方式。

The indexing method of conventional search engine always regards one page (or document) as primary indexing unit. Therefore, we cannot record the structural information between web pages. Furthermore, conventional search engines cannot deal with natural language questions effectively. For alleviating these two problems, first, we propose a structural indexing method to record the link structure information between pages. Second, we propose a Tri-link Mapping Model (TMM) to extract question implicitly structures embedded in user’s natural language questions. On the basis of question structures, we can map relevant Tri-links in the relevant website. We also propose a heuristic method to extract question structure by dividing question into three parts, and calculate the similarity with Tri-link based on the Position Dependent Similarity (PDS) and Position Independent Similarity (PIS). These two kinds of similarity measures are based on conventional Vector Space Model. The performance of structural indexing method and conventional indexing method will be analyzed according to our experiments. Experimental results show that structural indexing method can get higher precision than conventional indexing method for natural language search.

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