Abstract
As a person searches and reads documents of specific topic over the Internet, he/she might not understand or might be interested in some particular, confusing terms of the documents and might try to access some other references in order to clarify these confusing terms. However, there might be only few references linked to the documents via hyperlinks and these references usually provide limited ideas or concepts, which might cause the person to spend time on searching and filtering helpful references. In order to improve the efficiency and effectiveness for studying and comprehending the contextual content, this research develops a model for construction and visualization of augmented reality for the contextual content. Firstly, this research analyzes a great number of target documents and identifies some reference documents corresponding to the target ones. Secondly, a model is developed for analyzing these reference documents and transforming the documents into structured ones. After that, the proposed model can be applied for analyzing the topics and evaluating the characteristics of each structured document by using the vector space model and the neural network method. Finally, the proposed model can visually display the topics and characteristics of reference documents with respect to the target one to the reader. Based on the proposed model, this research develops a corresponding system to virtually construct the augmented reality for the target documents. Afterwards, this research designs some experiments to check the performance of the proposed model and constructed system. Consequently, the result of the experiments shows that the proposed model and system can effectively assist readers to understand the target document deeply and quickly.