臺灣博碩士論文加值系統

知識本體論為基礎的醫療照護語意問答系統之唐氏症應用，主要是應用知識本體論去組織與分類唐氏症相關的知識與資訊所建立而成的語意問答系統，這個系統提供使用者可以使用口語化的中文問句來輸入問題，系統會先將問句透過CKIP中研院斷詞系統的API將問句斷詞，分析出可以用在知識本體論上做推論的三個主要詞性用詞：主要醫療相關名詞、意圖動詞、疑問意圖這三個關鍵用詞；再藉由本篇論文所研究建立的SPARQL查詢語法在本體論上做推論，找出符合查詢句條件所對應的內容，當作問題的答案；此外，當問題無法準確分析出這些必須的關鍵詞時，會先使用SPARQL查詢句找出可能的相關詞並且重組問句回傳給使用者來引導使用者找到問題的答案。實驗的部份我們邀請專業婦產科醫生來協助評估問題回答的內容，評估的問題是透過網路上隨機選擇30題相關的唐氏症問題，答案的部分是藉由本篇研究所建立的語意問答系統以及目前線上有針對醫療相關的問答系統的網站做比較。最後，採用變異數的單變量分析得到結果為顯著，表示我們系統的回答結果有較好且正確的回答內容與資訊。

This study presents an ontology-based Semantic Question and Answer (Q&A) system applied to the Down Syndrome in Healthcare. We proposed a Q&A system which allows for users to ask questions in natural language, and the system will search the answers from the ontology by reasoning with related keywords from the natural language searched. In order to figure out what users’ questions meant we use the Classified Knowledge and Information Processing (CKIP) to tokenize words and to tag Part-Of-Speech (POS) in the questions. This Q&A system also uses a combination of three keywords, including medical terminologies, intention words which only exist in the domain of Medicine, and words of 5W1H, to analyze natural language questions. These three kinds of keywords allow our approach of answer extraction to reason in the ontology by formulated SPARQL queries. This system has been developed and tested in the Chinese language. The ontology is being adopted for classifying Down syndrome related information. We have carried out experiments to evaluate our approach of question analysis and answer extraction. The preliminary result shows that the excellent performance of our proposed approach.

1. Introduction + 1
1.1 Background + 1
1.2 Motivation + 3
2. Related works + 6
2.1 Question and Answer System + 6
2.1.1 Question Analysis + 6
2.1.2 Answers Analysis + 7
2.2 NLP-based Q&A system + 9
2.2.1 Lexical Analysis + 9
2.2.2 Semantic Analysis + 10
2.3 Semantic Web + 12
2.3.1 Ontology + 13
2.3.2 Building Ontology + 14
2.3.3 Application based on Ontology + 15
3. The Approach + 17
3.1 Skeleton of our Approach + 17
3.2 Question Analysis + 19
3.2.1 Word tokenization and Part of Speech (POS) Tagging in Chinese + 21
3.2.2 Keywords Identify + 21
3.3 Answer Extract + 26
3.3.1 SPARQL Queries + 26
3.4 Building Ontology + 31
4. Evaluation + 38
4.1 Evaluation of our approaches + 38
4.2 Evaluated with Different system + 40
5.Conclusion + 46
Reference + 49

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