臺灣博碩士論文加值系統

為改善過去詮釋資料僅對資料作描述，及未定義結構化資訊與其領域正規知識表達的映對關係，本研究使用語意的註記方式，以XML-based的註標語言，例如：RDF與OWL來表達資料間更豐富的關係，將資料視為概念，利用知識本體技術對資料背後的領域知識塑模，對概念下定義，以推論出資料間的隱含關係。知識本體技術已成為取代詮釋資料的方法之一，但是目前的知識本體設計，仍以專家的角度來建置，使得系統不理解使用者的語意，也無法滿足其需求。本研究在探討以使用者需求為出發點的知識本體工程建置，考慮使用者與專家對於專門領域的認知差異，因為使用者對於不熟悉的領域，會擁有不足的專業理性知識，所以會以感性想法來理解領域的內容。除領域知識本體外，發展認知知識本體架構，令所建置的知識本體庫不僅具備此專門領域的知識外，更滿足使用者的內隱需求，即包含其隱含的感性知識，使系統理解使用者的語意，檢索系統提供更多元以及人性化的查詢。從一開始認知蒐集階段，找出使用者所具備的「知」的程度，納入使用者所擁有的直覺、知覺、情緒等，作為知識本體設計的考量，利用感性工學(Kansei Engineering)的方法，透過問卷擷取使用者對領域中標的物的認知，找尋標的物特徵與使用者認知之間的關係，以設計認知知識本體模型。對於領域中標的物的描述，從單純的資料層級描述提升為語意層級的描述，系統的開發也從開發者角度轉換為使用者的角度，最後，以精確率與召回率評估此檢索系統，發現此系統能滿足不同專業程度的使用者，並縮小相關資訊的範圍，提供使用者一個符合不同程度需求的人性化智慧檢索系統。

This study develops a cognition-aware ontology in addressing poor emotional representation. Cognition-aware design refers to define the subjective emotional relationships between ordinary users and knowledge bases. The primary task of this study is translating human emotions into sensory-based properties that ontology intends to communicate through affective descriptions of their physical attributes. Since ontology consists of concepts and their relationships, the cognition-aware design must be applied to the design elements of concepts. To implement cognition-aware of ontology development, finding the elements of concepts such as physical attributes and their corresponding cognition properties are essential. Several methods have been utilized particularly in the expertise acquisition stage such as a questionnaire test, an affinity diagram, and statistical analysis. A walkthrough example of identifying dog breed is given to demonstrate the benefits of using a cognition-aware approach in the ontology building processes, including knowledge acquisition, concepts modeling, and ontology representation. Empirical lesson indicated that cognition-aware design is necessary especially when the users are non-experts. Experimental results indicated the new design helps users obtain appropriate results after inputting affective descriptions. The performance evaluation is positive.

目錄
摘要 I
英文摘要 II
誌謝辭 III
目　　錄 IV
圖 目 錄 VI
表 目 錄 VII
第一章　緒論 1
1.1 研究背景及動機 1
1.2 研究問題 2
1.3 研究目的 3
1.4 研究流程 4
第二章　文獻探討 5
2.1 使用者認知 5
2.2 知識擷取 7
2.3 知識表達 9
第三章　研究設計 14
3.1 系統架構 14
3.2 認知擷取 15
3.3 知識本體建置 18
3.4 概念檢索 19
第四章 家犬領域認知擷取 22
4.1 家犬認知知識本體塑模流程 22
4.2 問卷前置準備 24
4.3 使用者對家犬外觀認知問卷設計 32
第五章　知識本體庫設計 35
5.1 家犬特徵與感性詞彙相關分析之結果 37
5.2 家犬知識本體 42
5.3 感性概念檢索 46
5.4 檢索系統評估 49
第六章　結論與建議 53
6.1 研究結論與貢獻 53
6.2 研究限制 54
6.3 未來研究建議 55
參考文獻 57
附錄 64
A.『體型』變異數分析結果 64
B.『毛髮』變異數分析結果 66
C.『頭部』變異數分析結果 68
D.『耳朵』變異數分析結果 70
E.『吻部』變異數分析結果 72
F.『尾巴』變異數分析結果 74

圖 目 錄
圖1-1 語意式網站 1
圖1-2 研究流程 4
圖2-1 訊息處理模式的認知歷程 6
圖2-2 知識場觀點的認知歷程 6
圖2-3 產品特徵與感性 7
圖2-4 感性詞彙與產品組成特徵連結 9
圖3-1 系統架構圖 15
圖3-2 使用者需求管理歷程 15
圖3-3 感性工學模型 17
圖3-4 知識本體建置程序圖 18
圖3-5 知識本體庫設計 19
圖3-6 系統檢索流程 21
圖4-1 使用者認知為基礎之知識本體建置流程 23
圖4-2 家犬組成特徵 25
圖4-3 群數凝聚過程 28
圖4-4 家犬樹狀圖 29
圖4-5 家犬感性詞彙 32
圖4-6 問卷設計 34
圖5-1 知識本體庫與資料庫 35
圖5-2 知識本體庫設計與概念檢索 36
圖5-3 以PROTÉGÉ編輯家犬知識本體 42
圖5-4 認知知識本體分類 43
圖5-5 註記家犬知識本體的OWL檔 44
圖5-6 概念檢索介面 47
圖5-7 回傳符合的家犬 48
圖5-8 點選目標家犬 48
圖5-9 回傳符合的警衛犬 49

表 目 錄
表2-1 感性工學分類 8
表2-2 知識表達的四種形式 10
表2-3 知識本體定義 11
表3-1 RDQL檢索範例 20
表4-1 S-STRESS值與RSQ值 26
表4-2 家犬與其各群的中心距離 30
表4-3 問卷介紹 32
表4-4 篩選出的34對感性詞彙 33
表5-1 毛髮對於剛硬<-->柔軟之描述性統計量 39
表5-2 毛髮對於剛硬<-->柔軟之同質性檢定 39
表5-3 毛髮對於剛硬<-->柔軟之平均數相等穩健檢定 39
表5-4 毛髮對於剛硬<-->柔軟之描述性統計量 40
表5-5 尾巴對於剛硬<-->柔軟之同質性檢定 40
表5-6 尾巴對於剛硬<-->柔軟之變異數分析 40
表5-7 感性詞彙與各部位之相關性 41
表5-8 黃金拾獵犬外觀特徵註記範例 44
表5-9 黃金拾獵犬性格特徵註記範例 46
表5-10 警衛犬註記範例 49
表5-11 評估方法 50
表5-12 評估結果 51

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