Towards Enhancing Web Browsing Experience Using Deep Annotation and Semantic Visualization
| العنوان بلغة أخرى: |
نحو تحسين تصفح الويب باستخدام الترميز العميق والتصوير الالي |
|---|---|
| المؤلف الرئيسي: | Abed Alaal, Hanan M. (Author) |
| مؤلفين آخرين: | Al Agha, Iyad Mohammed (Advisor) |
| التاريخ الميلادي: |
2016
|
| موقع: | غزة |
| الصفحات: | 1 - 78 |
| رقم MD: | 736524 |
| نوع المحتوى: | رسائل جامعية |
| اللغة: | الإنجليزية |
| الدرجة العلمية: | رسالة ماجستير |
| الجامعة: | الجامعة الإسلامية (غزة) |
| الكلية: | كلية تكنولوجيا المعلومات |
| الدولة: | فلسطين |
| قواعد المعلومات: | Dissertations |
| مواضيع: | |
| رابط المحتوى: |
عدد مرات التحميل
93
| المستخلص: |
The Semantic Web annotation techniques focus on associating textual content with annotations sfrom external resources. These annotations included additional information on the annotated terms to help users, as well as machines, to better perceive the content of the text. The Semantic Web community has proposed several approaches to integrate semantic annotation into the Web browsing activity, and created what so called "Semantic Web browsers". Despite the affordances of existing Semantic Web browsers, they mostly focus on the semantic annotation process without considering effective ways to improve the user experience. This research builds on previous efforts on Semantic Web browsers, and seeks additional techniques to make the annotation process more constructive for Web browsing. We propose two extensions to the semantic annotation process: 1) Deep annotation, which aims to find more extended, correlated and indirectly observable entities even if these entities are not contained in the Web page. 2) a semantic network that visualizes the relationships between the different terms (entities) included in the Web page being browsed. We think that the proposed techniques will help the user better interpret the Web page content and utilize semantic annotations to gain broader knowledge. Our proposed annotation process was assessed by three human subjects, and results showed that 94.12% of the retrieved annotations were correct. Results also indicated that 95.44% of the terms included in the constructed semantic network was correct. |
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