Ontology一詞出自於哲學，原是用於對於存在的事物及這些事物的本性原理所作的整體性、統一性、基礎性的探究。而Ontology在知識工程領域中，是對於一個主題領域，建構其共通一致性的詞彙(基本術語)、屬性、關係、及其延伸的規則，作為知識的表徵與應用。
在以往的題庫系統中，對於試題的管理是基於試題的metadata的資訊，包括難度、鑑別度、門科分類、概念、認知層次類型（知識、理解、應用、綜合、分析、評量）…等等。而這樣的設計，試題之間是彼此獨立的，在試卷或題組所隱含的概念、知識，難以有系統組織化的呈現。也使得自動選題策略無法套上知識層面的思維。
在本研究中，將導入Ontology的理念來設計題庫系統，將試題所包含的知識(包含概念與關係)，以知識圖的形式表示，並以Ontology語言描述。透過了Ontology Model把試題之間的知識組織關係加以連繫、表徵，即可提供更高層次的試題管理，更進一步衍生出更具知識組織化的選題策略，如引導式題組、主題環繞式題組…等等。

The word "Ontology" stems from the philosophy, and it is originally used for research of integrity, unity, and foundational inquiry regarding the existence thing and natural disposition principles themselves. However, in the knowledge engineering domain, Ontology is used for regarding a subject domain; we construct the common uniform glossary (basic terminology), attributes, relations, and extensive rules for knowledge representation and application.
In previous studies on item bank, the item management is based on item metadata, which includes difficulty, discrimination, classification, concept, cognition level (knowledge, comprehension, application, analysis, synthesis, and evaluation), etc. Such design assumes that items in examination paper or item group are independent, thus the implying concepts and knowledge are hard to be represented systematically. It also causes automatic item selection strategy unable to be associated with knowledge.
In this research, we will adopt the idea of Ontology in designing item bank. The implying knowledge (including concept and relations) of item is represented by knowledge map and described by an Ontology language. Through Ontology Model, we can connect and represent the structural relationship of knowledge between items. Besides, it can provide a higher level for item management and develop the item selection question strategy based on the ontology structure of knowledge, such as item group of guidance, item group of topic surround and so on.

【目錄】
第一章 緒論 1
1.1 研究背景與動機 1
1.1.1 數位學習 1
1.1.2 測驗評量與題庫系統 2
1.1.3 本體論（Ontology） 3
1.2 研究目的 4
1.3 論文架構 4
1.4 成果及貢獻 4
第二章 背景知識 6
2.1 題庫系統 6
2.2 概念圖與知識圖 8
2.3 本體論 9
2.3.1 本體論的定義 9
2.3.2 本體論的應用 10
2.3.3 本體論的語言 11
第三章 系統架構 14
3.1 概觀 14
3.2 資料模型(Data Model) 15
3.3 系統模組 17
3.3.1 試題知識獲取模組(Item Knowledge Acquisition Module) 17
3.3.2 解析整合模組(Parsing and Integrating Module) 18
3.3.3 知識獲取模組(Knowledge Acquisition Module) 18
3.3.4 選題策略模組(Item Selection Strategy module) 18
3.3.5 試題產生模組(Item Generation Module) 19
第四章 系統實作 20
4.1 實作相關技術介紹 20
4.1.1 Web Services 20
4.1.2 Jena Semantic Web Framework 21
4.2 資料模型定義 22
4.3 試題知識獲取模組 22
4.4 解析整合模組 25
4.5 知識獲取模組 27
4.6 選題策略模組 27
4.6.1 引導式策略 28
4.6.2 主題環繞式策略 33
4.7 試題產生模組 36
第五章 應用情境 40
5.1 建構試題與知識圖 40
5.2 引導式選題策略 42
5.3 主題環繞式策略 44
5.4 產生試題 46
第六章 結論與後續研究方向 48
參考資料 49
附錄 52

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