本文關鍵詞： 探索式搜索 概念發(fā)現 概念匹配 概念合并 Rankclus算法　出處：《東北大學》2013年碩士論文　論文類型：學位論文

【摘要】：隨著Web2.0的快速發(fā)展,搜索引擎越來越受到用戶的廣泛應用和關注。目前的搜索引擎已經可以為目標明確的搜索提供高質量的搜索結果。然而,當用戶缺少針對目標領域的知識,或者搜索任務本身就要求很多分析和總結時,目前的搜索系統(tǒng)便無法直接的幫助用戶完成搜索過程。在這種情況下,用戶通常需要提交一些試探性的搜索請求,分析返回的結果,并決定下一步的搜索方向。這種搜索模式被稱為探索式搜索。針對探索式搜索,目前并沒有一個公認的解決方案。但其搜索的過程被認為分為：搜索聚合、支持發(fā)現、以及內容合成三個階段。其中,支持發(fā)現階段的主要任務是支持用戶發(fā)現包含能夠幫助其完成探索式搜索過程的資源。在支持發(fā)現的過程中,一個典型的方法是幫助用戶發(fā)現其所未知的概念。利用這些概念,用戶將可以進一步的找到和未知概念相關的文檔,并完成探索式搜索過程。 針對上述問題,本文在研究分面搜索的基礎上,提出了探索式搜索的概念發(fā)現的具體過程。并深入研究了概念匹配、概念合并的相關方法以及概念選擇的算法。針對用戶輸入的關鍵詞,選取出一組對目標領域描述最全面并且最有代表性的概念幫助用戶探索目標領域。 具體的,本文根據分面搜索具體過程所述,將探索式搜索概念發(fā)現過程總結為：構建知識庫、構建關鍵詞相關概念集、概念匹配、概念合并、概念選擇等階段。在構建知識庫階段將大眾分類法和維基百科結合起來為概念發(fā)現提供知識的支持。在概念匹配階段根據維基百科中對概念的定義構建關鍵詞維基百科相關概念模型,并提出了基于啟發(fā)式規(guī)則的概念匹配方法,獲得概念匹配結果集。在概念合并階段針對獲得概念匹配結果集,提出了基于啟發(fā)式規(guī)則的概念合并方法。在概念選擇階段根據大眾分類法中概念的使用情況,構建了概念,資源關系的信息網絡,并提出了基于Rankclus算法的概念選擇方法,將概念節(jié)點進行聚類和排序。根據概念的聚類和排序結果選擇一組對目標領域描述最全面并且最具代表性的概念作為概念發(fā)現結果集提供給用戶。針對獲得概念發(fā)現結果集中的概念,使用找到所需求文檔的搜索次數、結果文檔相關性兩個指標與原始方法以及直接排序不聚類的方法進行對比,以及概念發(fā)現提供的概念在用戶瀏覽文檔中出現比率的指標與直接排序不聚類的方法進行對比。實驗結果表明本文提出的概念發(fā)現方法能夠高效的幫助用戶探索目標領域。
[Abstract]:With the rapid development of Web2.0, search engines have attracted more and more attention from users. At present, search engines can provide high quality search results for targeted search. However, when users lack knowledge of target areas, Or when the search task itself requires a lot of analysis and summary, the current search system cannot directly help the user complete the search process. In this case, the user usually has to submit some tentative search requests. Analyze the results returned and determine the direction of the next search. This search pattern is called exploratory search. There is no recognized solution for exploratory search, but the search process is considered as: search aggregation. Support for discovery and content synthesis. The main task of supporting discovery phase is to support users to discover resources that can help them complete the exploratory search process. A typical approach is to help users discover their unknown concepts. With these concepts, users will be able to further find documents related to unknown concepts and complete the exploratory search process. In order to solve the above problems, this paper puts forward the concrete process of concept discovery of exploratory search on the basis of the research of facet search, and deeply studies the concept matching. According to the keywords entered by the user, a group of concepts that describe the target domain most comprehensively and most representative are selected to help the user explore the target domain. Concretely, according to the specific process of dividing search, this paper summarizes the discovery process of exploratory search concept as follows: building knowledge base, constructing keyword related concept set, concept matching, concept merging, Concept selection and other stages. In the stage of building knowledge base, the popular taxonomy and Wikipedia are combined to provide knowledge support for concept discovery. In the concept matching stage, the keyword dimension is constructed according to the definition of concept in Wikipedia. Basic encyclopedia related conceptual model, A concept matching method based on heuristic rules is proposed to obtain the concept matching result set. The concept merging method based on heuristic rules is proposed. In the stage of concept selection, the concept and resource relation information network is constructed according to the usage of the concept in the common classification, and the concept selection method based on Rankclus algorithm is proposed. The concept nodes are clustered and sorted. According to the clustering and sorting results of the concepts, a set of concepts that are the most comprehensive and representative of the target domain are selected to be provided to the user as the concept discovery result set. Discover the concept of a result set, Using the number of searches to find the required document, the correlation of the result document is compared with the original method and the method of direct sorting and non-clustering. The results show that the proposed concept discovery method can effectively help users to explore target areas.
【學位授予單位】：東北大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：TP391.3

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1 孟凡堯;探索式搜索中用戶概念發(fā)現方法研究[D];東北大學;2013年

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本文編號：1535667