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    政大機構典藏 > 資訊學院 > 資訊科學系 > 學位論文 >  Item 140.119/29677
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/29677


    Title: 從搜尋引擎查詢紀錄中學習Ontology
    Ontology Learning from Query Logs of Search Engines
    Authors: 陳茂富
    Contributors: 沈錳坤
    陳茂富
    Keywords: 搜尋引擎查詢紀錄
    學習Ontology
    Ontology learning
    Query log
    Date: 2001
    Issue Date: 2009-09-11 16:02:50 (UTC+8)
    Abstract: Ontology可用來組織、管理與分享知識,Ontology Engineering是一種建構Ontology的過程,建構的過程中,多數的工作需要人費時費力地去完成,因此利用機器來輔助Ontology Engineering成了一門重要的課題。使用Knowledge Discovery的方法協助Ontology Engineering建構Ontology的過程,稱為Ontology Learning,本論文中提出的Ontology Learning方法為分析使用者在搜尋引擎下關鍵字查詢時的行為,加上利用與查詢關鍵字有關的網頁資訊,以輔助建構Ontology。本論文中的Ontology由使用者所查詢的關鍵字組成,我們要learning的,則是這些關鍵字彼此之間的關係,其中有上義詞、下義詞與同義詞等等,因此,自動尋找關鍵字彼此之間的關係以輔助建構Ontology,即為我們提出本論文的目的。除此之外,本論文亦實作了完整的Ontology Learning系統,從一開始使用者查詢記錄的蒐集,關鍵字擷取與分析,關鍵字之間的關係判定,直到最後Ontology的產生,都將由系統自動完成。
    Ontology can be used to organize, manage and share knowledge. Ontology Engineering is the process of constructing Ontology. However, it’s usually a time-consuming and error-prone task. Thus, utilizing methods of Knowledge Discovery to help Ontology Engineering is called Ontology Learning. In this thesis, Ontology Learning process is done by using those pages related query terms and analyzing the querying behavior of users on search engines. The Ontology is organized by user query terms and relations among them. These relations we define are hyperonomy, hyponomy, synonymy and et al. Our goal of this thesis is to automatically learn the correct relations among these query terms. Besides, we implemented the complete system platform for Ontology Learning. The system can automatically collect logs, extract and analyze query keywords, and produce the final Ontology.
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    [2] Alfonseca, E. & Manandhar, S. (2002). Improving an Ontology Refinement Method with Hyponymy Patterns. Proc. of International Conference on Language Resources and Evaluation LREC’02.
    [3] Beeferman, D. & Berger, A. (2000). Agglomerative Clustering of a Search Engine Query Log. Proc. of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
    [4] Berendt, B., Mobasher, B., Spiliopoulou, M. & Wiltshire, J. (2001). Measuring the Accuracy of Sessionizers for Web Usage Analysis. Proc. of Workshop on Web mining, SIAM Conference on Data Mining.
    [5] Byrd, R. J. & Ravin, Y. (1999). Identifying and Extracting Relations in Text. Proc. of International Conference on Applications of Natural Language to Information Systems NLDB’99.
    [6] Chen, Z., Fu, A.W.C. & Tong F.C.H (2003). Optimal Algorithms for Finding User Access Sessions from Very Large Web Logs. World Wide Web: Internet and Web Information Systems, 6(3).
    [7] Chuang, S.L. & Chien, L.F. (2002). Towards Automatic Generation of Query Taxonomy: A Hierarchical Query Clustering Approach. Proc. of IEEE International Conference on Data Mining ICDM’02.
    [8] Chuang, S.L. & Chien, L.F. (2003). Enriching Web Taxonomies Through Subject Categorization of Query Terms from Search Engine Logs. Decision Support Systems, 35 (1).
    [9] Faure, D. & Nedellec, C. (1998). A Corpus-based Conceptual Clustering Method for Verb Frames and Ontology. Proc. of LREC Workshop on Adapting Lexical and Corpus Resources to Sublanguages and Applications.
    [10] Faure, D. & Poibeau, T. (2000). First Experiments of Using Semantic Knowledge Learned by ASIUM for Information Extraction Task Using INTEX. Proc. of Workshop on Ontology Learning.
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    [18] Lawrie, D. & Croft, W.B. (2000). Discovering and Comparing Topic Hierarchies. Proc. of RIAO 2000 Conference.
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    [21] Maedche, A. & Staab, S. (2001). Ontology Learning for the Semantic Web. IEEE Intelligent Systems, 16(2).
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    Description: 碩士
    國立政治大學
    資訊科學學系
    90753003
    90
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0090753003
    Data Type: thesis
    Appears in Collections:[資訊科學系] 學位論文

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