使用任何裝置、在任何地點和任何時間存取網頁內容是最近的趨勢。大部分的網路服務和網頁內容都是針對桌上型電腦而設計的，因此常常包含大量的多媒體檔案例如圖片、聲音和影像。但是裝置在網路連結方式、處理能力、儲存空間、顯示大小和格式的處理能力都不相同。 在很多情況下，原來在桌上型電腦呈現的網頁內容都不適合新的裝置（特別是行動裝置）。因此為了在不同的裝置和連線方式最佳化服務，內容改寫是必要的。
這篇研究主要是討論如何將使用者環境資訊應用到網頁的內容改寫。在本篇論文中，首先針對環境導向服務提供一個推論機制。透過這個推論機制，使用各種不同裝置的使用者可以獲得適當的內容。接著利用建立本體論增加彈性，並建立類別和個體之間的關係。最後利用語意網規則語言，去描述環境資訊彼此間關係。

The current and developing trend for consumers is to access Web content and applications anytime, anywhere, and on any device. Most Internet services and most World Wide Web content have been designed for desktop computers, and often contain rich media, such as images, audio, and video. However, devices differ in network connectivity, processing power, storage, display size, and format handling capabilities. In many cases, content designed for computers is not suitable for new (and often mobile) devices. Therefore, content adaptation is needed in order to optimize the service for different devices and access methods. 
This research discusses the way context is used for Web content adaptation. In this thesis, first we have proposed an inference mechanism for context-aware service. Through this inference mechanism, users using different devices can get appropriate content based on inference results. Second, we can demonstrate the correlation between classes and individuals and provide better scalability by means of building ontologies. Lastly, SWRL depends on ontology based rule language. Rules written based on SWRL can directly use an established object relationship from ontology.

Acknowledgements	I
中文摘要              III
Abstract               IV
List of Figures       VII
List of Tables	     VIII
1	Introduction	1
2	Related Work	5
2.1	The Semantic Web	5
2.1.1	XML	6
2.1.2	RDF	8
2.1.3	Ontology	11
2.2	The Methods of Client Capabilities Recognition	14
2.3	The Standard of Context Extraction	18
2.3.1	Composite Capability/Preferences profiles (CC/PP)	18
2.3.2	User Agent Profile (UAProf)	20
2.4 	Agent	24
2.5	Semantic Web Rule Language (SWRL)	28
2.6 	Jess	33
2.7	Protege	34
2.8	Examples of Content Adaptation System	36
3	Context-Aware system Architecture	39
4	Context inference and Content adaptation	42
4.1	Acquiring context information	43
4.2 	Building Ontologies and SWRL Rules	46
4.3 	Executing Jess inference and Updating the OWL Knowledge Base	48
4.4 	Providing appropriate content to users based on Inference results	52
5	Conclusion and Future Work	53
5.1	Conclusion	53
5.2	Future Work	54
6	Bibliography	55
7	Appendix A :UAProf for Nokia 6610 r100	59
8	Appendix B:Publication List	69
9	Appendix C: “INTERFERENCE-AWARE QoS MULTIPATH ROUTING FOR AD HOC WIRELESS NETWORKS”	71
10	Appendix D: “RRP:A Register Mechanism Routing Protocol in Wireless Sensor Network”	79

List of Figures
Figure 1-1	Content adaptation by the client	2
Figure 1-2	Content adaptation by the server	3
Figure 1-3	Content adaptation by the proxy	3
Figure 2-1	Semantic Web Stack	6
Figure 2-2	The RDF Triple	9
Figure 2-3	Protege Editor	35
Figure 3-1	System architecture	39
Figure 4-1	The mechanism of acquiring user context	44
Figure 4-2	Flow chart of user context data	46
Figure 4-3	Building individual and classes	47
Figure 4-4	Inference architecture	49
Figure 4-5	Presenting SWRL Rules as a Jess Rule	50

List of Tables
Table 2-1	Three Types of XML Tags	7
Table 2-2	Example of request header	15
Table 2-3	Examples of the User-Agent header	16
Table 2-4	Available agent platforms	27

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