臺灣博碩士論文加值系統

網路口碑為消費者透過部落格、討論區、電子佈告欄及電子郵件等虛擬平台，傳播對於某一產品或服務之評論。隨著網際網路的快速發展，網路口碑資訊倍增，使用者需花費更多時間過濾無關的資訊，過多的資訊提高消費者閱讀及判斷的困難度，也因為網路的匿名特性使口碑資訊較易受到質疑。本研究針對上述問題，嘗試(1)擴大資料檢索範圍，累積文件數量提高語意分析的可信度；(2)發展語意解析機制，分析口碑之內涵以利推薦。主要分為三項設計，首先，搜尋網路文件萃取本文資料，以利文件語意解析；再者，解析餐飲評鑑之評鑑項目，籍由評鑑項目擷取語意特徵，及量化特徵資訊；最後，針對解析之評鑑項目依知識工程方法進行知識塑模，擬定產品認知及口碑分析特徵之描述邏輯與語意規則，建立推薦之語意模型，配合產品推薦決策推導口碑推薦之結果。
本研究為驗證系統效能，選擇以餐飲美食評論為實驗對象，資料來源媒介為部落格平台，評估方式分為知識模型評估分析系統彈性及正確性、及文章分析正確性，挑選10家餐廳共取得有效文章數為387篇，其中系統與人工判讀相同文章數為341篇，文章正確率達88.11%；再者，由實驗結果顯示，知識本體結合推論規則的設計，可獲得正確口碑推薦，相較於資料庫系統較有彈性。

Electronic Word-of-Mouth (eWOM) is disseminating the comments of products or services through virtual platform by consumers. The virtual platform are various, such as blogs, discussion boards, electronic bulletin boards and e-mail etc. More information let users more difficult to read articles and make decision. Users need to spend more time to filter irrelevant information. This study tries to (1) Expand the scope of information retrieval and accumulate a large number of documents to improve the credibility of semantic analysis. (2) Develop mechanisms for the semantic analysis and analyzing eWOM to recommend. There are three main designs. First, search the web documents and extract the information in the articles, in order to facilitate semantic analysis of documents. Second, we analyze rating items about restaurant ratings in order to capture the semantic characteristics and converted the characteristics into quantities. Finally, this study utilizes knowledge-intensive approaches to represent restaurant recommendation models into ontologies. Develop description logic and semantic rules for cognitive of products and analysis of documents. Users get feedback through the knowledge of inferring.
This study verifies the accuracy of restaurant recommendation systems. We collected the articles of blog as experiment. Estimative methods are included that analyze flexibility and accuracy for this system, and analyze accuracy of articles. An ontology-based model with rule can capture accurate recommendations of eWOM and more flexible than RMDB.

目錄
摘要 I
ABSTRACT II
誌謝詞 III
目錄 IV
圖目錄 VI
表目錄 VII
第1章 緒論 1
1.1 研究背景與動機 1
1.2 研究問題 2
1.3 研究目的 2
第2章 文獻探討 4
2.1 網路口碑與網路文件 4
2.2 餐廳類型與餐飲評鑑 5
2.2.1 餐廳類型 5
2.2.2 餐飲評鑑 7
2.3 網路文件資料處理 10
2.4 文件語意與知識本體 12
第3章 研究設計 15
3.1 研究架構 15
3.2 網路文件處理 16
3.3 抽象化文件特徵 17
3.4 推薦設計階段 18
3.4.1 產品概念認知分析 19
3.4.2 產品推薦決策 20
3.4.3 推薦類型 21
第4章 建立知識本體模型及網路文件處理 25
4.1 建置餐廳概念及評價之知識本體 25
4.2 發展網路口碑推薦之語意規則 31
4.3 語料篩選設計流程 33
4.4 實作網路文件轉入知識本體 37
第5章 系統實作及評估 41
5.1 系統實作 41
5.2 系統評估 44
第6章 討論與結論 47
參考文獻 49
附錄 53
附錄A. 各篇文章語意推薦明細表 53


圖目錄
圖 2 1尼爾森市場調查影響全球外食族的決定因素 9
圖 2 2語意Web新體系結構 14
圖 3 1 研究架構圖 16
圖 3 2知識本體發展示意圖 22
圖 3 3 知識模型表達 23
圖 3 4 口碑分析與餐廳推薦之推導 24
圖 4 1概念分析圖 26
圖 4 2 CL知識階層 26
圖 4 3 餐廳知識模型表達 27
圖 4 4 使用Protégé發展知識本體 29
圖 4 5 Pellet歸類結果圖示 30
圖 4 6 Pellet推論實例結果 31
圖 4 7 規則示意圖 32
圖 4 8 protégé語意規則呈現 32
圖 4 9 語料篩選設計流程 33
圖 4 10文件特徵擷取與推論流程 38
圖 4 11 網路口碑資料收集及文件樣式 39
圖 4 12 句子詞彙比對及文章評價結果 39
圖 4 13 網路口碑資料轉入知識本體之呈現 40
圖 5 1 推薦系統檢索流程 41
圖 5 2 餐廳評論搜尋介面 42
圖 5 3 主詞語料設定 42
圖 5 4 述詞語料設定 42
圖 5 5 餐廳推薦資訊查詢-依推薦類型 43
圖 5 6 結果回應介面 44

表目錄
表2 1 餐廳類型(以菜色區分) 6
表2 2 餐廳評鑑指標項目 10
表3 1 歸納後評鑑指標項目 17
表3 2 餐廳推薦條件 20
表4 1 餐廳推薦評價問題之屬性 28
表4 2 主題詞彙表 34
表4 3中研院平衡語料庫詞類標記集(部分資料) 36
表4 4 述詞詞彙表(部分摘錄) 37
表5 1 排除之餐廳資訊 45
表5 2 文章正確率評估結果 46

[中文部分]
許中川、陳景揆，2001，「探勘中文新聞文件」，資訊管理學報，第七卷，第二期：103-122頁。
戚玉樑，2005，「知識擷取與知識表達協同程序於建構本體的概念架構」，資訊管理學報，第十三卷，第二期：193-215頁。
戚玉樑，2006，「知識擷取與知識表達協同程序於建構知識本體的概念架構」，資訊管理學報，第十三卷，第二期：193-215頁。
戚玉樑、蔡明宏，2007，「以文件為對象的概念萃取程序建立知識本體的雛型架構」，資訊管理學報，第十四卷，第一期：47-66頁。
經濟部商業司，2006，「經濟部95年度商業發展情勢年報」，台北市：中華經濟研究院。
詹益政、黃清峯，2005，「餐旅業經營管理」，台北市：五南圖書。
蕭玉倩，1999，「餐飲概論」，台北市：揚智。
尼爾森市場調查公司，2007，「口碑傳播 最佳銷售利器」，網頁資料：
http://tw.cn.acnielsen.com/site/news20071011.htm。
尼爾森市場調查公司，2009，「影響全球外食族的決定因素」，網頁資料：
http://tw.nielsen.com/site/news20090409.shtml。
謝忠道，2006，廣州新快報米其林專題採訪，網頁資料：http://mypaper.pchome.com.tw/news//bordeaux/3/1273006201/20060907173044。
傅依傑，2007，「美食聖經「查加」比米其林銷，網頁資料：http://www.wretch.cc/blog/juyacw/9006370。



[英文部分]
Arndt, J. “Role of Product-Related Conversations in the Diffusion of a New Product,” Journal of Marketing Research (4:3), 1967, pp. 291-295.
Brown, J. J., and Reingen, P. H. “Social Ties and Word-of-Mouth Referral Behavior,” Journal of Consumer Research (14:3), 1987, pp.350-362.
Corinna Cortes, Vladimir Vapnik “Support-Vector Networks,” Machine Learning (20:3), 1995, pp. 273-279.
Chandrasekaran, B., Josephson, J.R., and Benjamins, V.R. “What are ontologies, and why do we need them?,” IEEE Intelligent Systems (14:1), 1999, pp. 20-26.
Chi, Y. -L. “A consumer-centric design approach to develop comprehensive knowledge-based systems for keyword discovery," Expert Systems with Applications (36:2), 2009, pp. 2481-2493.
Decker, S., Melnik, S., Harmelen, F.V., Fensel, D., Klein, M., Broekstra, J., Erdmann, M., and Horrocks, I. “The Semantic Web: The Roles of XML and RDF,” Internet Commputing (4:4), 2000, pp. 63-74.
Doob, L. W. “Public Opinion and Propaganda,” New York: Henry Holt, 1948.
Dorre, J., Gerstl, R., and Seiffert, R. “Text Mining: Finding Nuggets in mountains of textual data,” Proceedings of the 5's ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1999, pp. 398-401.
Eric Brill, “ A Corpus-based Approach to Language Learning,” phD Dissertation, University of Pennsylvania, 1993.
Gelb, B. D. and Sundaram,S. “Adapting to word of mouse,” Business Horizon (45:4), 2002, pp. 21-25.
Gilly, M. C., Graham, J. L., Wolfinbarger, M. F., and Yale, L. J. “A Dyadic Study of Interpersonal Information Search,” Journal of the Academy of Marketing Science (26:2), 1998, pp.83-100.
Gruber, T.R., “Towards Principles for the Design of Ontologies Used for　Knowledge Sharing,” International Journal of Human-Computer Studies (43:5-6), 1995, pp. 907-928.
Gruninger, M., Atefi, K., and Fox, M.S., “Ontologies to Support Process　Integration in Enterprise Engineering,” Computational and Mathematical　Organization Theory (6:4), 2000, pp. 381-394.
Hennig-Thurau, T. and Walsh, G. “Electronic Word-of-Mouth: Motives for and Consequences of Reading Customer Articulations on the Internet,” International Journal of Electronic Commerce (8:2), 2003, pp. 51-74.
Henning-Thurau, T., Gwinner, K. P., Walsh, G., and Gremler, D. D. “Electronic Word-of-Mouth via Consumer-opinion Platforms: What Motivates Consumers to Articulate Themselves on the Internet?,” Journal of Interactive Marketing (18:1), 2004, pp. 38-52.
Herr, P.M., Kardes, F.R., and Kim, J. “Effects of Word-of-Mouth and Product-Attribute Information on Persuasion: An Accessibility-Diagnosticity Perspective,” Journal of Consumer Research (17:4), 1991, pp. 454-462.
Johnson, C., Surlemont, B., Nicod, P., & Revaz, F. “Behind the stars: A concise typology of Michelin restaurants in Europe” Cornell Hotel and Restaurant Administration Quarterly (46:2), 2005, pp. 170-187.
Kiecker, P. and Cowles, D. “Interpersonal communication and personal influence on the Internet: A framework for examining online word-of-mouth,” Journal of Euro-Marketing(11:2), 2002, pp. 71-88.
Lin, J., Fox M.S., and Bilgic, T., “A Requirement Ontology for Engineering Design,” Concurrent Engineering: Research and Applications(4:4), 1996, pp. 279-291.
Maedche, A., & Staab, S. “Ontology learning for the semantic web,” IEEE Intelligent Systems, 2001, pp. 72-79.
Morris, H. “Blogging burgeons as a form of web expre-ssion,“ U.S. News ＆World Report (130:2), 2001, pp. 52.
Noy, N.F. and McGuinness, D.L. “Ontology Development 101: A Guide to Creating Your First Ontology,” Stanford Knowledge Systems Laboratory Technical Report KSL-01-05, 2001.
Rashid, A.M., Albert, I., Cosley, D., Lam, S.K., McNee, S.M., Konstan, J.A., and Riedl, J. “Getting to know you: learning new user preferences in recommender systems,” in San Francisco, Proceedings of the IUT 02, CA, 2002, pp.127-134.
Smith, B., and Welty, C. “Ontology: Towards a new synthesis,” Proceedings of the International Conference on formal ontology in Information system, Ogunquit, Maine:ACM, 2001, pp. 3-9.
Van der Vet, P.E., and Mars, N.J.I. “Bottom-up construction of ontologies,” IEEE Transaction on Knowledge and Data Engineering (10:4) 1998, pp. 513-526.
Westbrook, R.A. “Product/consumption-based affective responses and postpurchase processes,” Journal of Marketing Research (24:3), 1987, pp. 258-270.
Wu, S. H., Day, M.Y., Tsai, T.H. and Hsu, W.L. “FAQ-centered Organizational Memory,” in Matta, N. and Dieng-Kuntz, R. (ed.), Proceeding of Knowledge Management and Organizational Memories, Kluwer Academic Publishers, Boston, 2002, pp. 103-112.
網頁資料
Battey, J. “Weblogs mix creative expression with practic-al information,” 1999
(available online at
http://www.infoworld.com/cgi-bin/displayNew.pl?/hotsites/hotextra991101.htm).
網頁資料
Berners-Lee “Artificial Intelligence and the Semantic Web,” 2006
(available online at http://www.w3.org/2006/Talks/0718-aaai-tbl/Overview.html).
網頁資料
Merriam Webster “Merriam-Webster's Words of the Year,” 2004
(available online at http://www.merriam-webster.com/info/04words.htm).
網頁資料
Zagat Survey “Rated establishments,” 2007
(available online at
http://www.zagat.com/about/about.aspx?menu=content_policy&LNK=Policy).