政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/29677

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/29677

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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

請使用永久網址來引用或連結此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/29677

題名:	從搜尋引擎查詢紀錄中學習Ontology Ontology Learning from Query Logs of Search Engines
作者:	陳茂富
貢獻者:	沈錳坤陳茂富
關鍵詞:	搜尋引擎查詢紀錄學習Ontology Ontology learning Query log
日期:	2001
上傳時間:	2009-09-11 16:02:50 (UTC+8)
摘要:	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.
參考文獻:	[1] Agrawal, R., Imielinski, T. & Swami, A. (1993). Mining Association Rules Between Sets of Items in Large Databases. Proc. of ACM SIGMOD Conference on Management of Data. [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. [11] Gomez-Perez, A. & Manzano-Macho, D. (2003). A Survey of Ontology Learning Methods and Techniques. Technical Report, Institute of Computer Science, Leopold Franzens University of Innsbruck. [12] Hahn, U.& Klemens, S. (1998). Towards Text Knowledge Engineering. Proc. of Conference on Artificial Intelligence AI’98. [13] Hahn, U. & Schulz, S. (2000). Towards Very Large Terminological Knowledge Bases: A Case Study from Medicine. Proc. of Canadian Conference on Artificial Intelligence AI’00. [14] Hearst, M.A. (1992). Automatic Acquisition of Hyponyms from Large Text Corpora. Proc. of International Conference on Computational Linguistic. [15] Huang, C.K., Chien, L.F. & Oyang, Y.J (2003). Relevant Term Suggestion in Interactive Web Search Based on Contextual Information in Query Session Logs. Journal of the American Society for Information Science and Technology, 54(7). [16] Khan, L. & Luo, F. (2002). Ontology Construction for Information Selection. Proc. of IEEE International Conference on Tools with Artificial Intelligence ICTAI`02. [17] Kietz, J.U., Maedche, A. & Volz, R. (2000). A Method of Semi-Automatic Ontology Acquisition from a Corporate Intranet. Proc. of EKAW’2000 Workshop on Ontologies and Texts. [18] Lawrie, D. & Croft, W.B. (2000). Discovering and Comparing Topic Hierarchies. Proc. of RIAO 2000 Conference. [19] Lonsdale, D., Ding, Y., Embley, D.W. & Melby, A. (2002). Peppering Knowledge Sources with SALT: Boosting Conceptual Content for Ontology Generation. Proc. of AAAI Workshop on Semantic Web Meets Language Resource. [20] Maedche, A. & Staab, S. (2000). Discovering Conceptual Relations from Text. Proc. of European Conference on Artificial Intelligence ECAI’00. [21] Maedche, A. & Staab, S. (2001). Ontology Learning for the Semantic Web. IEEE Intelligent Systems, 16(2). [22] Maedche, A. & Steffen, S. (2003). Ontology Learning. Handbook on Ontologies in Information Systems, S. Staab & R. Studer (eds.). Springer. [23] Morin, E. (1999). Automatic Acquisition of Semantic Relations Between Terms from Technical Corpora. Proc. of International Congress on Terminology and Knowledge Engineering TKE’99. [24] Nobecourt, J. (2000). A Method to Build Formal Ontologies from Texts. Proc. of EKAW’2000 Workshop on Ontologies and Texts. [25] Sanderson, M. & Croft, B. (1999). Deriving Concept Hierarchies from Text. Proc. of ACM International Conference on Research and Development in Information Retrieval SIGIR’99. [26] Wagner, A (2000). Enriching a Lexical Semantic Net with Selectional Preferences by Means of Statistical Corpus Analysis. Proc. of Workshop on Ontology Learning OL’01. [27] Wen, J.R., Nie, J.Y. & Zhang, H.J. (2001). Clustering User Queries of a Search Engine. Proc. of International on World Wide Web WWW’01. [28] Wen, J.R., Nie, J.Y. & Zhang, H.J. (2002). Query Clustering Using User Logs. ACM Transactions on Information Systems, 20(1)
描述:	碩士國立政治大學資訊科學學系 90753003 90
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0090753003
資料類型:	thesis
顯示於類別:	[資訊科學系] 學位論文

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