隨著網路技術的成熟以及普及化發展，使得網頁數量呈現爆炸性的成長，使用者想在這浩瀚無垠的網路世界中快速的找到所想要的資訊，必須透過搜尋引擎的力量才能達成。
搜尋引擎藉由資訊擷取的技術蒐集網路上的網頁並擔任資訊提供者提供資訊給使用者，使用者只需輸入關鍵字即能獲取所需的資訊。
然而，對於相同概念的文件來說，網頁作者以及使用者所使用的字詞不一樣會造成使用者無法獲得搜尋引擎中其他描述相同概念的網頁文件，
這個問題即是字詞使用差異上的不同所造成，而解決這類問題的方法即是查詢擴展，將使用者所輸入的查詢自動擴展成更多的字詞，以期能搜尋到更為完備的資訊。

本研究所提出的方法，是以結合本體論以及關聯法則進行查詢擴展，希望能改善字詞使用差異的問題並擷取到更多描述同一概念的網頁文件數量，滿足使用者的需求。
以建構出來的本體為主，
並使用網路爬行器蒐集所需的網頁文件為資料集合，再進行探勘字詞之間的關聯法則，並結合本體之中字詞之間的語意關係以及字詞之間的關聯法則關係做為推薦字詞的基礎，
提供給使用者一查詢擴展的推薦機制，協助使用者進行查詢。

With the development of Internet, web pages grow rapidly. In
order to search information they need the users often depend on
search engine. A search engine collects web pages in Internet by
information retrieval techniques, and serves as an information
provider to users. However, regarding web pages of the same
concept, the words used by authors and users use may be different.
This is a "word dismatch" problem which prevents users from
retrieving all web pages of the same concept. The solution is
"query expansion"(QE). QE can expand users' queries and let users
gain more complete information.

This research proposes one method for combining ontology with
association rules to perform QE. It can resolve the word dismatch
problem and retrieve more web pages of the same concept and
satisfy users' needs. The method we proposed is based on ontology,
and uses spider to collect web pages as the data set. After the
spider's operation is finished, we will mine the association rules
between words. We provide one QE's recommendation mechanism which
combines words' semantic relationships within ontology with
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