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系統識別號 U0002-0207200721504200
中文論文名稱 數位學習環境下之語意延伸答問系統
英文論文名稱 Semantic-Extended Question Answering System for e-Learning
校院名稱 淡江大學
系所名稱(中) 資訊工程學系博士班
系所名稱(英) Department of Computer Science and Information Engineering
學年度 95
學期 2
出版年 96
研究生中文姓名 王文男
研究生英文姓名 Wen-Nan Wang
學號 892190033
學位類別 博士
語文別 英文
口試日期 2007-06-05
論文頁數 101頁
口試委員 指導教授-王英宏
委員-廖弘源
委員-陳朝欽
委員-林偉川
委員-葛煥昭
委員-施國琛
委員-王英宏
中文關鍵字 語意答問系統  Link Grammar  詞彙網路(WordNet)  數位學習 
英文關鍵字 Semantic QA  WordNet  Link Grammar  e-learning 
學科別分類 學科別應用科學資訊工程
中文摘要 在數位學習環境中,學習者比較容易缺乏同儕與教師間的互動,當遇到課程相關的問題時,往往得不到即時的解決,必須透過線上即時問答系統,或者透過留言版或電子郵件來提出問題;但這必須得仰賴老師或者教學助教即時的在線上提供回應,學習者才能得到解答。如何提供自動化的學習輔助及自我學習機制,則是目前數位學習環境的一個重要發展目標;因此如何設計一個能提供自動化即時回答與教材相關的答問機制,對於學習者的學習成就將會相當助益。
因此本論文提出一個支援語意認知方法的答問系統(Semantic QA),採用知識本體技術設計資料結構課程教材,以期能切要回答學習者以自然語言所提問之課程知識。
根據上述目標,本論文針對語意分析所採用相關研究方法為:(1)使用Link Grammar Parser來分析學習者所提之問句句法資訊建構句法樹,(2)分析問句類型取得標的片語並建構一個語意樹以表示問句句意,(3)藉由WordNet取得相似字清單以得到更相關的語意資訊,(4)最後將語意樹與課程知識本體比對取得學習者所提問題之答案。
而針對於知識本體內容完整性,本論文設計提供知識本體延伸模組分析網路上相關主題之教材,以豐富答問系統知識本體;此外並提供教師回饋機制,教師可藉由此模組觀察系統是否可正確回答學習者問題,並可補充答問系統相關之複合字、問句類型、相似字清單等問句分析的相關資訊。
本論文發展的問答系統具備以下特性:(1)瞭解發問者以自然語言形式所提出的問題,(2)增加回答問題的準確率,(3)可以延伸問句分析的相關資訊,(4) 建立自動化的問答系統以輔助學習。
英文摘要 Different from those in the traditional learning, learners in the e-learning environment usually have less opportunity to interact with their teachers or fellows. If learners have questions, they have to seek for answers through the on-line question answering system or post their questions on message board or use e-mail to ask questions. However, teachers or tutors can not solve each question immediately if they are not online simultaneously. Therefore, how to build an automatic assistance for learning and provide a self-paced learning mechanism are the objectives in today’s e-learning environment. For this reason, constructing an automatic question answering mechanism will be very helpful for learners to get solutions instantly.
According to the above motivation, this thesis proposes a Semantic-extended Question Answering system (Semantic QA) to analyze learners’ questions and find the relevant answer from the target course ontology which applies the Data Structure to construct teaching materials.
To achieve this goal, the following shows the research steps. Firstly, Link Grammar Parser is applied to analyze the Syntactic Information of an input sentence and form a syntactical tree. Secondly, the question type is analyzed to find the target phrase and then form a Semantic Tree to represent the meaning of the question. Thirdly, WordNet is used at this stage to generate the Similar Word Lists to extend relevant meaning. Lastly, the Semantic Tree will be mapped to the Data Structure course ontology and find the relevant contents in order to answer them to learners.
The Semantic QA system has the following characteristics: (1) understand learners’ questions in the form of natural language, (2) enhance the accuracy of solutions, (3) make the extensibility of the related question analysis information and (4) establish the automatic learning mechanism.
論文目次 Contents

1 Introduction 1
2 Theoretical Background 5
2.1 Link Grammar 5
2.2 WordNet 12
2.3 Ontology 13
3 System Architecture 18
3.1 Background Research 18
3.2 System Architecture Design 20
3.2.1 User Interface: 22
3.2.2 Syntactic Analysis: 23
3.2.3 Semantic Analysis: 24
3.2.4 Ontology Design and Extension: 24
4 System Design 25
4.1 Syntax Analysis 27
4.1.1 Replace Compound Word 29
4.1.2 Get Syntactic Information 31
4.1.3 Restore Compound Word 32
4.1.4 Construct Syntactical Tree 32
4.2 Semantic Analysis 34
4.2.1 Question Sentence Pattern Match 34
4.2.2 Semantic Tree Construction 37
4.2.3 Similar Word Sense Extension 40
4.2.4 Map the Data Structure Course Ontology 44
4.3 Ontology Design and Extension 48
4.3.1 Data Structure Course Ontology Design 48
4.3.2 Build Domain Terminology 51
4.3.3 Ontology Extension Module 52
4.4 Instructor Feedback for System 55
5 Evaluation 57
6 Conclusions and Future Works 61
7 Bibliography 64
8 Appendix A. Publication List 67
9 Appendix B. 71
10 Appendix C. 93




List of Figures
Figure 2 1 Words and connectors in a dictionary 6
Figure 2-2. All linking requirements are satisfied 6
Figure 2 3. A simplified form of Figure. 2 7
Figure 2 4. The link grammar output Linkage example 1 10
Figure 2 5. The link grammar output Linkage example 2 10
Figure 2 6. The Link Grammar output sample through Phrase Parser component -1 10
Figure 2 7. The Link Grammar output sample through Phrase Parser component -2 11
Figure 2 8. An example of Ontology using KAON 17
Figure 3 1. System Functional Blocks 19
Figure 3 2. System Architecture 21
Figure 3 3. User Interface Layer 22
Figure 4 1. System Process Flow 26
Figure 4 2. Syntactical Analysis Layer 29
Figure 4 3. Incomplete Linkage example with “fist in first out” 30
Figure 4 4. Complete Linkage with “fist-in-first-out” 31
Figure 4 5. The output syntactic information with “constituents =1” 31
Figure 4 6. The output syntactic information with “constituents =2” 31
Figure 4 7. The XML form of the syntactic information 32
Figure 4 8. The Syntactical Tree of the example question sentence 33
Figure 4 9. The sample of retrieved target phrase -1 35
Figure 4 10. The sample of retrieved target phrase -2 35
Figure 4 11. Semantic Linking Unit 37
Figure 4 12. The Node presentation 38
Figure 4 13. The nodes dependency representation 38
Figure 4 14. The Semantic Tree example 39
Figure 4 15. Add similar words to Semantic Tree 43
Figure 4 16. The Refined Semantic Tree 46
Figure 4 17. The Partially Mapped Answers 47
Figure 4 18. Concept Layer of the Data Structure Course Ontology 50
Figure 4 19. Instance layer example of the Data Structure Course Ontology 51
Figure 4 20. Ontology Extension Module 53
Figure 4 21. Instructor Feedback for System 55


List of Tables

Table 2 1. The words and linking requirements in a dictionary 7
Table 2 2. WordNet Synset sample of “Add” 12
Table 4 1. Compound Words List 30
Table 4 2. Question Sentence Pattern Matching Table 34
Table 4 3. Similar Word List 40
Table 5 1. Evaluation with other famous systems 60
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[Toutanova 2000] Toutanova, K. and Manning, C. D., “Enriching the Knowledge Sources Used in a Maximum Entropy Part-of-Speech Tagger,” in Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora (EMNLP/VLC-2000), Hong Kong, 2000, pp.63-70
[Wang 2005] Wang, Y.-H., Wang, W.-N., and Lin, C.-H., “An Intelligent Semantic Agent for E-Learning Message Communication,” Journal of Information Science and Engineering, Special Issue on E-Learning, vol. 21, no. 5, pp.1031-1051, 2005
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[Yen 2007] Yen, Y.-H., “Semantic-aware Ontology for QA System: a Statistical Approach,” M.S. thesis, Department of Computer Science & Information Engineering, Tamkang University, Taiwan, 2007
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