在相關回饋的領域當中，Rocchio演算法因容易實作且有一定的效能水準，在查詢擴展的研究中被廣為使用與研究，而該演算法依據相關文件詞頻及非相關文件詞頻之資訊，藉此產生新的查詢擴展字詞，並進行再次檢索。然而在進行再次檢索時，查詢擴展當中可用的字詞資訊並不會被使用於檢索的過程當中，因此查詢擴展字詞之間是否存在其他可以加以利用的資訊並應用於檢索當中，仍然是個有趣的議題。近年來，語意搜索的研究陸續被提出，主要考量到查詢字詞所涵蓋的語意概念，而非單純使用查詢字詞本身。因此，本研究基於現有的自然語言相關研究，運用WordNet之字詞語意資訊，計算查詢擴展字詞間之語意相似度，接著透過分群萃取出查詢擴展之概念資訊，依據本研究提出之方法計算出文件與查詢擴展之概念匹配程度，並將之使用於修正原始查詢擴展之排序。最後透過實驗證明，本研究所提出之方法與原始查詢擴展之效能相比之下，皆有更好的檢索效果。;In the field of relevance feedback, Rocchio’s query expansion method is simple and effective. It has been widely used in information retrieval. Rocchio’s algorithm produces query expansion according to term frequency in the feedback documents provided by the user and uses it to retrieve documents. Although Rocchio’s method are effective, the terms’ information such as semantics are not utilized in the retrieval process. This study aims to analyze the terms’ information of query expansion and uses the terms’ information for document re-ranking. Recently, the idea of semantic search is getting more and more popular. It is concerned with the semantic meaning of the query terms. Based on NLP technique, this study utilizes WordNet which is a large lexical database of English to calculate semantic similarity between query terms and extract concepts of query expansion by using clustering algorithm. The proposed method calculates concept score between query expansion and each document, and uses the calculated concept score for document re-ranking. The results of experiments show that the proposed method of this study is effective in document retrieval.
