臺灣博碩士論文加值系統

近年來糖尿病病患逐年增加，且隨著網路技術迅速發展，使用者可透過瀏覽器以及搜尋引擎來查詢相關資訊，但目前搜尋引擎主要遇到兩個障礙(1)語言障礙；(2)搜尋障礙。對於搜尋障礙方面，由於在資訊爆炸的時代，搜尋引擎所查詢回來的結果種類太多，內容繁雜。使用者必須要逐一的瀏覽內容，才能確認所搜尋回來的結果是否正確，是不是使用者本身要的內容資訊，這是一種費時費工的工作。因此問答系統技術越來越被受到重視，問答系統的目的是使用者提出任意問題，系統給予一個正確的答案。對於語言障礙方面，使用者使用搜尋引擎的另一個問題使用者可透過搜尋引擎來查詢相關資訊，但獲得的資訊只限於和查詢關鍵字相同的語言。因此造成搜尋語言障礙的問題，並導致使用者可搜尋的資訊量減少。本論文的主要目的是要結合特定領域的知識本體、問答系統和跨語言資訊檢索技術，開發一個跨語言糖尿病資訊問答系統，提供使用者輸入查詢問句，並透過自然語言處理程序了解使用者想要什麼樣的資訊，再藉由多語言本體提供合適正確的答案。實驗結果證明我們所提出方法具有70％以上的正確率在回答糖尿病知識問題上。

In recent years, diabetic patients are increasing quickly. And with the rapid development of network technology, users can query information through the browser and search engine. However, there are two main barriers for search engines. The first one is language barrier and the second one is search barrier. In search barrier, the query results have many species and the contents are too complex that users must look for the contents of web pages in order to confirm the search results are correct and the results are users needed which waste much time and work. Therefore, a technology of question answering system has been more attention. A question answering system provides relevant information to users for correct answers questions. On the other hand, users use search engines exist the other problem that the access information is limited to the same keywords and query languages. This is a language barrier which leads to information reduction. The main purpose of this study combined domain-specific of the ontology for the question answering system with cross-language information retrieval technology to develop a diabetes information cross- language question answering system. The system offers users to inputting query questions and to understand what information users want through the natural language processing based on multi-language ontology to provide appropriate and correct answer. The primary experiments prove that our system has more than 70% accuracy for on line language query and answer for diabetic information.

第1章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 4
1.3 論文架構 4
第2章 文獻探討 6
2.1 知識本體(Ontology) 6
2.1.1 知識資源描述框架 8
2.1.2 本體描述語言(Web Ontology Language) 9
2.1.3 SPARQL協定與RDF查詢語言 11
2.2 問答系統 (Question Answering System) 12
2.3 跨語言資訊檢索 (Cross-Information Retrieval) 15
2.4 本體標注 (Ontology Annotation) 17
2.5 決策法則的推導 (Decision Rule-based Induction) 18
第3章 研究方法 19
3.1 研究架構 19
3.2 問題分析模組(Question Analysis Module) 20
3.3 語言轉換模組(Language Transform Module) 24
3.4 資訊檢索模組(Information Retrieval Module) 25
3.5 實例說明 29
第4章 實驗 32
4.1 實驗環境 32
4.2 知識本體之建置 32
4.3 系統介面 33
4.4 實驗設計 34
4.5 實驗結果與分析 34
4.6 討論 35
第5章 結論與未來研究 36
參考文獻 38
附錄A 44
附錄B 45
附錄C 47
附錄D 56

表 1 OWL子語言描述 10
表 2 SPARQL語法之舉例查詢結果 12
表 3 常見網路翻譯資源之比較 17
表 4 CKIP斷詞之舉例 21
表 5 詞彙重組規則 22
表 6 重要詞性對照表 23
表 7 實例結果 31

圖 1網際網路上各種語言文件所占比例 2
圖 2 知識本體之舉例 7
圖 3 RDF三元結構之舉例 9
圖 4 SPARQL查詢語法之舉例 11
圖 5 傳統QAs主要模組處理流程 13
圖 6 以知識本體為基礎QAs主要模組處理流程 14
圖 7 研究架構 19
圖 8 問題分析模組流程 20
圖 9 查詢擴張之舉例 24
圖 10 語言轉換模組流程 25
圖 11 糖尿病知識本體架構 33
圖 12 系統介面 33

[1]許智浩(2008)，運用具學習能力的領域知識本體於線上FAQ系統，碩士論文，朝陽科技大學資訊管理系碩士論文，台中。
[2]陸韋銘(2009)，以本體論為基礎運用模糊正規化與SPARQL查詢語言之研究，碩士論文，朝陽科技大學資訊管理系碩士論文，台中。
[3]孫維駿(2009)，基於知識本體之智慧型Semantic Web研究與建構，碩士論文，國立臺南大學資訊工程學系，臺南。
[4]陳慶龍(2007)，可供語意搜尋之部分知識建構，碩士論文，國立成功大學工程科學系，臺南。
[5]蔡宜玲(2010)，建置知識本體之國小綜合活動學習領域教學，碩士論文，南華大學資訊管理系，嘉義。
[6]謝光良(2009)，FML-based智慧型健康飲食規劃代理人之研究，碩士論文，國立臺南大學資訊工程學系，臺南。
[7]黎煥中、鐘順麒、林世鐸、蘇矢立、廖培勇、杜思德、林素蘭、林淑滿(2007)，RF糖尿病血糖自我監測之決策知識管理，全國計算機會議。
[8]馬心婷、羅麗萍、竹幼婷、吳家嫙、羅欣怡，名醫的糖尿病聖經，棋碁文化事業有限公司，第116頁~第148頁。
[9]賴育民、白蕙菁、黃國欽、李育臣、菜嘉一、李曜暄、黃俊傑，糖尿病完全百科，晨星出版有限公司台北編輯室，第9頁~第148頁。
[10]行政院衛生署統計室
http://www.mohw.gov.tw/cht/DOS/，2012/05/01。
[11]Excite翻譯
http://www.excite.co.jp/world/，2012/05/01。
[12]Yahoo翻譯
http://tw.babelfish.yahoo.com/translate_txt?ei=UTF-8&doit=done&intl=1&trtext=&lp=en_zt，2012/05/01。
[13]CKIP中文詞知識庫小組
http://godel.iis.sinica.edu.tw/CKIP/，2012/05/01。
[14]S. J. Athenikos and H. Han (2010), "Biomedical question answering: A survey," Computer Methods and Program in Biomedicine, Vol. 99, No. 1, pp. 1-24.
[15]F. M. Aymerich, G. Fenu, and S. Surcis(2008), "An Approach to A Cloud Computing Network, " Proceedings of 1st International Conference on the Applications of Digital Information and Web Technologies, pp. 113-118, August4-6.
[16]A. Frank, H. U. Krieger, F. Xu, H. Uszkoreit, B. Crysmann, B. Jorg and U. Schafer (2007), "Question answering from structured knowledge sources," Journal of Applied Logic, Vol. 5, No.1, pp. 20-48.
[17]J. Fu, J. Xu, and K. Jia (2009), "Domain Ontology Based Automatic Question Answering," International conference on Computer Engineering and Technology, pp. 346-349.
[18]O. Ferrandez, C. Spurk, M. Kouylekov, I. Dornescu, Sergio Ferrandez, M. Negri, R. Izquierdo, D. Tomas, C. Orasan, G. Neumann, B. Magnini and J. L. Vicedo (2011), "The QALL-ME Framework: A specifiable-domain multilingual Question Answering architecture," Web Semantics: Science, Services and Agents on the World Wide Web, Vol. 9, No. 2, pp. 137-145.
[19]S. Ferrandez, A. Toral, O. Ferrandez, A. Ferrandez and Rafael Munoz (2009), "Exploiting Wikipedia and EuroWordNet to solve Cross-Lingual Question Answering," Information Sciences, Vol. 179, No. 20, pp. 3473-2488.
[20]Q. L. Guo and M. Zhang (2009),"Question answering based on pervasive agent ontology and Semantic Web," Knowledge-Based Systems, Vol. 22, No. 6, pp. 443-448.
[21]S. L. Huang and Y. H. Tsai (2010), "Designing a Cross-language Comparison Shopping Agent," Decision Support Systems, Vol. 50, No. 2, pp. 428-438.
[22]K. Z. Jia and J. Z. Xu (2011), "A Chinese Question-Answering System with Question Classification and Answer Clustering," Seventh International Conference on Fuzzy Systems and Knowledge Discovery, pp. 2692-2696.
[23]K. Kishida (2005), "Technical issues of cross-language information retrieval a review," Information Processing & Management., Vol. 41, No. 3, pp. 433-455.
[24]O. Kolomiyets and M. F. Moens (2011), "A survey on question answering technology from an information retrieval perspective," Information Sciences, Vol. 181, No. 24, pp. 5412-5434.
[25]W. H. Lu, R. S. Lin, Y. C. Chan and K. H. Chen (2007), "Using Web resources to construct multilingual medical thesaurus for cross-language medical information retrieval," Decision Support Systems., Vol. 45, No. 3, pp. 585-595.
[26]P. Moreda, H. Llorens, E. Saquete and M. Palomar (2010), "Combining semantic information in question answering systems," Information Processing and Management, Vol. 47, No. 6, pp. 870-885.
[27]J. Michael, A. Berry, S. Gordon, Linoff (1997), "Data Mining Techniques: for Marketing, Sales, and Customer Support , "John Wiley & Sons, Inc. New York, NY, USA.
[28]A. G. Tapeh and M. Rahgozar (2008), "A knowledge-based question answering system for B2C eCommerce," Knowledge-Based Systems, Vol. 37, No. 10, pp. 946-950.
[29]S. Y. Teng, and G. W. Bian (2008), "Integrating Query Translation and Text Classification in a Cross-Language Patent Retrieval System, " Proceedings of NTCIR-7 Workshop Meeting, December 16–19, 2008, Tokyo, Japan.
[30]D. Wu and D. HE (2010), "A Study of Query Translation using Google Machine Translation," International Conference on Computational Intelligence and Software Engineering, Wuhan, pp. 1-4.
[31]Y. C. Wang, R. T. H. Tsai and W. L. Hsu (2009), "Web-based pattern learning for named entity translation in Korean-Chinese cross-language information retrieval," Expert Systems with Applications., Vol. 36, No. 2, pp. 3990-3995.
[32]National Library of Medicine(NLM)
http://www.nlm.nih.gov/, 2012/05/01.
[33]Medical Subject Headings(MeSH)
http://www.nlm.nih.gov/mesh/MBrowser.html, 2012/05/01.
[34]PubMed
http://www.ncbi.nlm.nih.gov/sites/entrez, 2012/05/01.
[35]Meta Map / SemRep
http://www.nlm.nih.gov/mesh/meshhome.html, 2012/05/01.
[36]GENIA
http://www-tsujii.is.s.u-tokyo.ac.jp/GENIA/, 2012/05/01.
[37]Pub Med Central
http://www.pubmedcentral.nih.gov/, 2012/05/01.
[38]SNOMED
http://www.nlm.nih.gov/snomed/, 2012/05/01.
[39]NCIEVS
http://www.nci.nih.gov/cancerinfo/terminologyresources, 2012/05/01.
[40]Gene Ontology
http://www.geneontology.org/, 2012/05/01.
[41]Clinical Questions Collection
http://clinques.nlm.nih.gov/About.html, 2012/05/01.
[42]Google Translation
http://translate.google.com.tw/?hl=zh-TW&tab=wT#, 2012/05/01.
[43]WordNet
http://wordnet.princeton.edu/, 2012/05/01.
[44]Wikipedia
http://zh.wikipedia.org/zh-tw/Wikipedia:%E9%A6%96%E9%A1%B5，2012/05/01。
[45]EuroWordNet
http://www.illc.uva.nl/EuroWordNet/corebcs/topont.html, 2012/05/01.