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研究生:陸韋銘
研究生(外文):Wei-Ming Lu
論文名稱:以本體論為基礎運用模糊正規化與SPARQL查詢語言之研究
論文名稱(外文):The Study of Documents Query System Based on Domain Ontology Using FFCA and SPARQL
指導教授:陳榮靜陳榮靜引用關係
指導教授(外文):Rung-Ching Chen
學位類別:碩士
校院名稱:朝陽科技大學
系所名稱:資訊管理系碩士班
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2010
畢業學年度:98
語文別:英文
論文頁數:67
中文關鍵詞:模糊正規化分析本體知識常見問答集糖尿病SPARQL
外文關鍵詞:FAQFuzzy Formal Concept AnalysisOntologyDiabetesSPARQL
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在網際網路的盛行中,用戶分享了問題和解答,讓不懂得解決問題的使用者很快獲得相關資訊。但是在尋找資訊的同時需要花費時間成本在瀏覽網頁與文件上,以決定是否為有用資訊。一般民眾對於慢性病(如糖尿病、高血壓等)知識並不是很足夠,民眾須尋求相關資料,來充實相關知識。在目前醫院宣導慢性病防護的方式,包括醫護人員解說,書本、網站宣導等。其中網站架設的常見問答集(Frequently Asked Questions, FAQ)是最快呈現給民眾,但是常見問答集的多寡影響到瀏覽時間成本。為了解決在搜尋時能傳回符合使用者問題解答,而不是花時間在尋找資訊上,本研究以本體論(Ontology)建立糖尿病衛生教育領域知識,將FAQ建置於本體論中。當使用者提出問題字句,利用模糊正規化(Fuzzy Formal Concept Analysis, FFCA)分析出問題與衛生教育文件之符合程度,針對問題回應文件。針對本體論的架構與回應文件特性,本研究利用SPARQL(SPARQL Protocol and RDF Query Language) 查詢語言從本體論中將文件存取出來,幫助使用者在尋找文件時能夠快速找到所需要的文件。
The Internet, a rapidly growing phenomenon, provides an unlimited resource to discover information on chronic diseases. There are abundance of web forums that discuss questions and answers about disease facts, drug
information, and even coping mechanisms. However, this process can be time consuming and might not provide simple, comprehensible facts. With chronic diseases, many people do not know the proper ways to care for themselves because they lack of the knowledge of their disease (such as diabetes, high blood pressure, etc.). In hospitals, it is possible for chronic diabetes patients to learn care information from the books, websites, or hospital employees. An online service system FAQ (Frequently Asked Questions) was constructed on some hospitals’ websites that can provide immediate information to users. In this study, we set up document query system based on domain ontology for diabetes’ health education. We used former patient questions and analyzed the degree of similarity between the questions asked and the content of the diabetes health education document using the Fuzzy Formal Concept Analysis (FFCA) method. In this study, we used SPARQL (SPARQL Protocol and RDF Query Language) language to get documents in the framework of ontology. In our research, we built the domain ontology by collecting 50 diabetes health education documents and test forty-six sentences to evaluate the precision of our system.
中文摘要 ................................................. I
Abstract................................................. II
致謝 .................................................... IV
List of Figures........................................... X
List of Tables ......................................... XII
Chapter 1 Introduction ................................... 1
1.1 Background ........................................... 1
1.2 Motivation ........................................... 2
1.3 Purpose .............................................. 2
1.4 The framework of the thesis .......................... 3
Chapter 2 Related literature ............................. 4
2.1 Semantic and ontology ................................ 4
2.2FAQ (Frequently Asked Questions) ..................... 11
2.3 Diabetes mellitus and health education .............. 15
2.4 Related technologies ................................ 16
2.4.1 Chinese Knowledge and Information Processing (CKIP) ......................................................... 16
2.4.2 FCA (Formal Concept Analysis) ..................... 17
2.4.3 Fuzzy FCA (Fuzzy Formal Concept Analysis) ......... 20
2.4.4 SPARQL (SPARQL Protocol and RDF Query Language) ....22
Chapter 3 Research framework ............................ 26
3.1 Construct domain ontology ........................... 27
3.2 Question pre-processing ............................. 32
3.3 Generation fuzzy context ............................ 33
3.4 Search and return document .......................... 38
Chapter 4 Experiments and Discussions ................... 40
4.1 Experiments ......................................... 41
4.1.1 The experiments based on TF ....................... 47
4.1.2 The experiments based on TF-IDF ................... 50
Chapter 5 Conclusions and Futures work .................. 56
References .............................................. 57
Appendix ................................................ 64
Publications ............................................ 67
Figure 1 W3C framework ..................................................................... 5
Figure 2 The element of ontology ......................................................... 7
Figure 3 The level of generality ontology ............................................ 8
Figure 4 FCA concept lattices ............................................................. 20
Figure 5 The concept lattice of fuzzy concepts ................................. 22
Figure 6 An example of SPARQL ...................................................... 24
Figure 7 The system workflow of FAQ system .................................. 26
Figure 8 Diabetes Health education ontology framework ............... 29
Figure 9 Build class and attribute ...................................................... 30
Figure 10 Setup Data Types ................................................................ 30
Figure 11 Setup instance ..................................................................... 31
Figure 12 The format of OWL ........................................................... 31
Figure 13 The workflow of question processing ............................... 33
Figure 14 An example generate context table ................................... 34
Figure 15 An example of SPARQL language processing ................. 39
Figure 16 Part of the construct ontology and FAQ documents method ................................................................................ 41
Figure 17 The threshold value test by TF weight ............................. 49
Figure 18 The threshold value test ..................................................... 55
Table 1 Diabetes mellitus detect standard ......................................... 16
Table 2 The type of pos tagging .......................................................... 17
Table 3 FCA context ............................................................................ 19
Table 4 Membership value context of FFCA context ....................... 21
Table 5 FFCA-Context removed from membership values lower than α-cut=0.6 .............................................................................. 21
Table 6 SPARQL query to identify question of patients .................. 25
Table 7 The syntax of “Why diabetics have fruity breath odor?” .. 32
Table 8 Pattern set of analysis words ................................................. 32
Table 9 The syntax of “Why diabetics have fruity breath odor?” FCA context .................................................................................. 34
Table 10 Membership value of FFCA context “Why diabetics have fruity breath odor?” .................................................................... 35
Table 11 FFCA-Context removed from membership values lower α-cut=0.04 ..................................................................................... 38
Table 12 SPARQL query to get FAQ in ontology ............................. 39
Table 13 The example of user questions ............................................ 42
Table 14 The memberships value of the example for “Why diabetics have fruity breath odor?” ........................................................... 43
Table 15 Example for “Why diabetics have fruity breath odor?” health education documents ....................................................... 43
Table 16 The memberships value of the example for “How do I know the medicines are effective?” ............................................ 44
Table 17 Example for “How do I know the medicines are effective?” Health Education documents ...................................................... 45
Table 18 Retrieval related matrix ...................................................... 45
Table 19 The number of correct and error documents is returned using Term Frequency from threshold value from 0.01 to 0.4 by step 0.01 ................................................................................... 48
Table 20 The number of correct and error documents is returned using Term Frequency–Inverse Document Frequency from threshold value from 0.01 to 0.2 by step 0.01 ............................ 53
Table 21 The number of correct and error documents is returned using Term Frequency–Inverse Document Frequency from threshold value from 0.21 to 0.4 by step 0.01 ............................ 54
Table 22 The forty-six of user questions ............................................ 64
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