臺灣博碩士論文加值系統

語意網(Semantic Web)希望提供全球資訊網上的資源，具有語意的標記，使其變成電腦能夠理解的資料。本體論(ontology)能夠提供一組定義完整的字彙作為「資料的描述性資訊」(metadata)，使全球資訊網上的資料能夠明確地被定義。藉由定義共享的、通用的本體論，使人與電腦之間不只能達到語法上的交談，更能達到語意上的交談，準確地作溝通。因此語意網的成功發展因素，在於能否迅速且容易地建構本體論。本研究認為本體論是一個階層式概念的資訊組織網路，能夠有效地使用在一個有系統的領域知識裡，因此它在各個領域中扮演重要的角色。本文提出一個可以快速的自動產生本體論之概念，從網際網路中截取相關領域網頁文件，利用HTML(Hypertext Markup Language)的標籤選擇所需要的關鍵字詞，藉由這些字詞在文件中之關係 建構一個屬於語意網中的語意本體。實驗步驟包括：(1)從網頁標籤特徵來找出相關的關鍵字，(2)移除stop-word，(3)找出可以代表該領域的Key-term然後運用TF-IDF(Term Frequency-Inverse Document Frequency)來判斷Key-term的權重值，(4)結合本研究所提出的遞迴式自適應共振理論網路(Recursive Adaptive Resonance Theory network，RART)來做分群，(5)最後使用布林模式的集合限制存在之方式判別上下階層之關連性，就是超類別與子類別的階層架構關係，上層的概念比較一般化，在下層的概念比較具體化，到了最底層時就變成了一個具體的實例，最後再透過Java套件Jena將結果輸出為符合RDF(Resource Description Framework)檔案。

Ontology can be used to build metadata which describes data about data and offers a group of glossaries with individual definition that covers a certain knowledge area. It not only transfers syntax of words but also accurately transfers semantic data between human users and the network. Hence, the usefulness of the semantic web depends on whether the domain of ontology can be constructed effectively and correctly. In this thesis we propose an automated method to construct an domain ontology. First, we collected domain-related web pages from the Internet. Secondly, we use the HTML (Hypertext Markup Language) tag labels to choose meaningful terms from the web pages. Next, we use these terms to construct an domain ontology by calculating a TF-IDF (Term Frequency-Inverse Document Frequency) to find the weight of terms, using a RART network (Recursive Adaptive Resonance Theory Network) to cluster terms. Each group of terms will find a candidate keyword for ontology construction. Boolean operations locate individual keywords in a hierarchy. Finally, the system outputs an ontology in a Jena package using an RDF (Resource Description Framework) format. The primary experiment indicates that our method is useful for an domain ontology creation.

中文摘要 I
Abstract II
誌謝 III
Table of Contents V
List of Tables VII
List of Figures VIII
Chapter 1 Introduction 1
1.1 Background and motivation 1
1.2 Objective 2
1.3 The framework of thesis 4
Chapter 2 Literature Review 6
2.1 Semantic web 6
2.2 Ontology 8
2.3 Related technologies 11
2.3.1 Content feature of web pages 11
2.3.2 Information classification model 11
2.3.3 Single value decomposition 13
2.3.4 Adaptive Resonance Theory network: ART 14
Chapter 3 Constructing Ontology 17
Chapter 4 Experimental Result 24
4.1 First experiment 24
4.2 Second stage experiment 32
Chapter 5 Conclusion and Future Work 39
References 40
Appendix 1 44
Appendix 2 47

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