博碩士論文 100423008 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:18 、訪客IP:18.116.230.40
姓名 賴靜怡(Ching-Yi Lai)  查詢紙本館藏   畢業系所 資訊管理學系
論文名稱 自動建立Ontology應用於User Profile建立
(Automatically Constructing Ontology Applied to User Profile Creation)
相關論文
★ 網路合作式協同教學設計平台-以國中九年一貫課程為例★ 內容管理機制於常用問答集(FAQ)之應用
★ 行動多重代理人技術於排課系統之應用★ 存取控制機制與國內資安規範之研究
★ 信用卡系統導入NFC手機交易機制探討★ App應用在電子商務的推薦服務-以P公司為例
★ 建置服務導向系統改善生產之流程-以W公司PMS系統為例★ NFC行動支付之TSM平台規劃與導入
★ 關鍵字行銷在半導體通路商運用-以G公司為例★ 探討國內田徑競賽資訊系統-以103年全國大專田徑公開賽資訊系統為例
★ 航空地勤機坪作業盤櫃追蹤管理系統導入成效評估—以F公司為例★ 導入資訊安全管理制度之資安管理成熟度研究-以B個案公司為例
★ 資料探勘技術在電影推薦上的應用研究-以F線上影音平台為例★ BI視覺化工具運用於資安日誌分析—以S公司為例
★ 特權帳號登入行為即時分析系統之實證研究★ 郵件系統異常使用行為偵測與處理-以T公司為例
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   [檢視]  [下載]
  1. 本電子論文使用權限為同意立即開放。
  2. 已達開放權限電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。
  3. 請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。

摘要(中) 網路的發展使得資料量愈來愈龐大,使用者要從大量的資訊中找到目標是非常困難的,因此本研究利用自建的ontology架構來建立user profile ontology來幫助記錄使用者興趣。本研究主要分兩大部份:自動建立ontology與user profile建立。在自動建立ontology部份,過去大部份的ontology都由領域專家手動建立,非常耗時與維護更新不易,因此本研究提出使用動態文件階層分群來建立ontology架構,其中我們針對部份流程進行優化,改善其準確度與效能。User profile部份,本研究利用自建的ontology架構再加上使用者接觸過的文章與行為因子來建立explicit profile與implicit profile,並且此user profile可以表達使用者不同時期的興趣與達到使用者興趣的延伸,可以幫助使用者找到符合興趣的文章。
摘要(英) The development of Internet makes the amount of data get larger. It is very difficult for user to find the data they want among the huge amount of data. This study creates user profile ontology by using the self-built ontology to record user’s interest. This study consists of two parts: automatically constructing ontology and user profile creation. In the first part, most ontology was created manually by experts, it was not only time-consuming but also hard to maintain and update. Therefore, this study constructs ontology by dynamically hierarchical clustering on document, and we optimize parts of the process to improve the accuracy and performance. In the part of user profile creation, this study use the self-built ontology, the articles that user had read and behavioral factors to create explicit profile and implicit profile. And these user profiles represent the interests in the different periods or the extension of interests of user, it help user to find the article what they may have interest.
關鍵字(中) ★ 階層分群
★ 動態分群
關鍵字(英) ★ Ontology
★ Explicit profile
★ Implicit profile
論文目次 一、緒論 1
1-1 研究背景 1
1-2 研究動機 2
1-3 研究目的 3
1-4 研究方法 4
1-5 論文架構 5
二、文獻探討 6
2-1 文件特徵選取 6
2-1-1 NGD 6
2-1-2 K-core 8
2-2 自建搜尋引擎 9
2-3 文件分群 10
2-3-1 文件分群技術分類 10
2-3-2 文件分群應用-動態文件階層分群 12
2-4 使用者輪廓 15
2-4-1 顯性輪廓 17
2-4-2 隱性輪廓 17
2-5 本體論 18
三、研究方法與系統架構 20
3-1 系統架構 20
3-2 自動建立本體論 21
3-2-1 資料前處理 22
3-2-2 文件概念分群 22
3-2-3 建置分類學與文件階層分群 23
3-3使用者輪廓計算模組 24
3-3-1 顯性輪廓計算 24
3-3-2 隱性輪廓計算 28
3-4 使用者輪廓本體論 29
3-4-1 建立使用者輪廓本體論架構 30
3-4-2 更新使用者輪廓本體論 31
四、實驗結果與討論 33
4-1 資料集介紹 33
4-1-1 Reuters-21578 33
4-1-2 維基百科 34
4-2 評估方法 34
4-3 實驗環境 36
4-4 文件前處理實驗結果 36
4-5 文件概念分群實驗結果 38
4-6 Explicit profile實驗 39
4-6-1 α值參數調整實驗 39
4-6-2 Explicit profile 不同domain下實驗 40
4-6-3Explicit profile 同domain下實驗 42
4-6-4 Explicit profile 於同domain與不同domain下建立之比較 43
4-7 Implicit profile實驗 43
4-7-1 Implicit profile實驗-Technology and applied sciences 44
4-7-2 Implicit profile實驗- Geography and places 45
4-7-3 Implicit profile於不同domain比較 45
4-8 Explicit profile 時間性實驗 46
4-8-1 UIA之φ參數調整 46
4-8-2 EI門檻值調整 47
4-8-3 Explicit profile時間性實驗 48
五、結論與未來研究方向 51
5-1 結論 51
5-2未來研究方向 52
5-3管理意涵 54
參考文獻 55
參考文獻 [1] R. Baeza-Yates, "Modern information retrieval : the concepts and technology behind search," ed, 2011.
[2] L. Cao, "In-depth behavior understanding and use: the behavior informatics approach," Information Sciences, vol. 180, pp. 3067-3085, 2010.
[3] C.-L. Chen, F. S. Tseng, and T. Liang, "An integration of WordNet and fuzzy association rule mining for multi-label document clustering," Data & Knowledge Engineering, vol. 69, pp. 1208-1226, 2010.
[4] P.-I. Chen and S.-J. Lin, "Automatic keyword prediction using Google similarity distance," Expert Systems with Applications, vol. 37, pp. 1928-1938, 2010.
[5] R. L. Cilibrasi and P. M. Vitanyi, "The google similarity distance," IEEE Transactions on Knowledge and Data Engineering, vol. 19, pp. 370-383, 2007.
[6] R. Gil-Garcia and A. Pons-Porrata, "Dynamic hierarchical algorithms for document clustering," Pattern Recognition Letters, vol. 31, pp. 469-477, 2010.
[7] M. Golemati, A. Katifori, C. Vassilakis, G. Lepouras, and C. Halatsis, "Creating an ontology for the user profile: Method and applications," First IEEE International Conference on Research Challenges in Information Science, pp. 407-412, 2007.
[8] T. R. Gruber, "Toward principles for the design of ontologies used for knowledge sharing?," International journal of human-computer studies, vol. 43, pp. 907-928, 1995.
[9] K. M. Hammouda and M. S. Kamel, "Efficient phrase-based document indexing for web document clustering," IEEE Transactions on Knowledge and Data Engineering, vol. 16, pp. 1279-1296, 2004.
[10] C.-K. Hsu, G.-J. Hwang, and C.-K. Chang, "Development of a reading material recommendation system based on a knowledge engineering approach," Computers & Education, vol. 55, pp. 76-83, 2010.
[11] H.-Y. Jeong, C.-R. Choi, and Y.-J. Song, "Personalized Learning Course Planner with E-learning DSS using user profile," Expert Systems with Applications, vol. 39, pp. 2567-2577, 2012.
[12] L. Khan and F. Luo, "Ontology construction for information selection," Proceedings of the 14th IEEE International Conference on Tools with Artificial Intelligence, pp. 122-127, 2002.
[13] H.-j. Kim, S. Lee, B. Lee, and S. Kang, "Building concept network-based user profile for personalized web search," IEEE International Conference on Computer and Information Science, pp. 567-572, 2010.
[14] K.-T. Leung and D. L. Lee, "Deriving concept-based user profiles from search engine logs," IEEE Transactions on Knowledge and Data Engineering, vol. 22, pp. 969-982, 2010.
[15] T.-P. Liang, Y.-F. Yang, D.-N. Chen, and Y.-C. Ku, "A semantic-expansion approach to personalized knowledge recommendation," Decision Support Systems, vol. 45, pp. 401-412, 2008.
[16] Y.-C. Liu, X.-L. Wang, and B.-Q. Liu, "A feature selection algorithm for document clustering based on word co-occurrence frequency," Proceedings of 2004 International Conference on Machine Learning and Cybernetics, pp. 2963-2968, 2004.
[17] D. L. McGuinness and F. Van Harmelen, "OWL web ontology language overview," W3C recommendation, vol. 10, p. 10, 2004.
[18] S. E. Middleton, N. R. Shadbolt, and D. C. De Roure, "Ontological user profiling in recommender systems," ACM Transactions on Information Systems (TOIS), vol. 22, pp. 54-88, 2004.
[19] N. F. Noy, M. Sintek, S. Decker, M. Crubézy, R. W. Fergerson, and M. A. Musen, "Creating semantic web contents with protege-2000," Intelligent Systems, IEEE, vol. 16, pp. 60-71, 2001.
[20] A. Pons-Porrata, R. Berlanga-Llavori, and J. Ruiz-Shulcloper, "Topic discovery based on text mining techniques," Information processing & management, vol. 43, pp. 752-768, 2007.
[21] J. R. Quinlan, "Induction of decision trees," Machine learning, vol. 1, pp. 81-106, 1986.
[22] G. Salton, "Dynamic document processing," Communications of the ACM, vol. 15, pp. 658-668, 1972.
[23] G. Salton and C. Buckley, "Term-weighting approaches in automatic text retrieval," Information processing & management, vol. 24, pp. 513-523, 1988.
[24] S. B. Seidman, "Network structure and minimum degree," Social networks, vol. 5, pp. 269-287, 1983.
[25] P. Shoval, V. Maidel, and B. Shapira, "An ontology-content-based filtering method," International Journal of Information Theories and Applications, pp. 51-63, 2008.
[26] J.-H. Su, B.-W. Wang, C.-Y. Hsiao, and V. S. Tseng, "Personalized rough-set-based recommendation by integrating multiple contents and collaborative information," Information Sciences, vol. 180, pp. 113-131, 2010.
[27] X. Tang and Q. Zeng, "Keyword clustering for user interest profiling refinement within paper recommender systems," Journal of Systems and Software, vol. 85, pp. 87-101, 2012.
[28] J. Trajkova and S. Gauch, "Improving Ontology-Based User Profiles," in RIAO, 2004, pp. 380-390.
[29] H. Wen, L. Fang, and L. Guan, "A hybrid approach for personalized recommendation of news on the Web," Expert Systems with Applications, vol. 39, pp. 5806-5814, 2012.
[30] F. Wu and D. S. Weld, "Automatically refining the wikipedia infobox ontology," Proceedings of the 17th international conference on World Wide Web, pp. 635-644, 2008.
[31] Y. C. Yang, "Web user behavioral profiling for user identification," Decision Support Systems, vol. 49, pp. 261-271, 2010.
[32] 李浩平, "運用NGD建立適用於使用者回饋資訊不足之文件過濾系統," 中央大學資訊管理學系學位論文, 2011.
[33] 陳信夫, "基於字詞關係動態建立階層分群," 中央大學資訊管理學系學位論文, 2011.
[34] 詹欣逸, "利用WordNet判斷字詞包含關係─應用於動態文件分群," 中央大學資訊管理學系學位論文, 2012.
[35] 鄭奕駿, "離線搜尋 Wikipedia 以縮減 NGD 運算時間之研究," 中央大學資訊管理學系學位論文, 2012.
指導教授 林熙禎(Shi-Jen Lin) 審核日期 2013-7-22
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明