當資訊需求者透過網際網路閱讀一份資料時，其往往因無法理解該資料之特定字句、或對於該資料之特定內容感到興趣而進一步搜尋該特定字句或內容之相關資料。然而，此些相關資料往往僅為特定說明網頁而非多元之參考資料，故此類特定說明網頁乃可能無法滿足資訊需求者之需求，而致使資訊需求者額外花費時間搜尋其欲釐清之特定字句或內容的相關資料。
為改善上述過程之效率及效能，本研究乃提出一套「文件內容之擴增實境與其視覺化模式」。此模式乃包含「現行補充資料內容解析」前置作業及「補充資料內容分析及特性呈現」方法論，而此方法論又包含「補充資料內容解析」、「補充資料面向解析」、「補充資料基本特性評估」、「補充資料特質視覺化」等四大階段。具體而言，此方法論乃先針對資訊需求者所欲閱讀之目標文件及對應之相關資料進行內容解析，並根據解析結果釐清目標文件所對應之補充資料；之後，此方法論乃以詞彙關聯性及向量空間模型等概念為基礎將此些補充資料予以分群，以推論此些補充資料所對應之主題、面向，再以類神經網路方法評估各補充資料內容之多項特性；最後，此方法論乃將各補充資料之面向及特性等資訊以視覺化方式呈現。
其次，本研究乃根據此模式開發對應之系統平台，以針對資訊需求者所欲閱讀之目標文件建構對應之擴增實境，進而呈現目標文件之補充資料及對應之關鍵資訊。之後，本研究乃以品質主題相關之資料測試所提出之模式及所開發之系統的績效表現，並得知此模式及系統能提升資訊需求者閱讀目標文件之效率，亦能使其更有效地瞭解目標文件之所有細項內容。

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.

1. 陳妍妤，詹智凱，劉立，徐建業，2009，「以詞的關聯性為基礎之文件自動分類系統應用於網路癌症衛教資訊分類」，中華民國癌症醫學會雜誌，第25卷，第5期，頁350-356。
2. 葉怡成，2003，「類神經網路模式─應用與實作」，第8版，儒林圖書公司。
3. 周美西，1996，「以類神經網路預測混凝土非破壞檢測之抗壓強度」，碩士論文（指導教授：張奇偉、葉怡成），中華工學院土木工程研究所。
4. Miao, D., Duan, Q., Zhang, H. and Jiao, N., 2009, "Rough set based hybrid algorithm for text classification," Expert Systems with Applications, Vol. 36, No. 5, pp. 9168-9174.
5. Özel, S. A., 2011, "A web page classification system based on a genetic algorithm using tagged-terms as features," Expert Systems with Applications, Vol. 38, No. 4, pp. 3407-3415.
6. Li, C. H. and Park, S. C., 2009, "An efficient document classification model using an improved back propagation neural network and singular value decomposition," Expert Systems with Applications, Vol. 36, No. 2, pp. 3208-3215.
7. Saracoglu, R., Tutuncu, K. and Allahverdi, N., 2008, "A new approach on search for similar documents with multiple categories using fuzzy clustering," Expert Systems with Applications, Vol. 34, No. 4, pp. 2545-2554.
8. Wang, Z. and Sun, X., 2011, "Document classification algorithm based on MMP and LS-SVM," Procedia Engineering, Vol. 15, pp. 1565-1569.
9. Chen, L., Guo, G. and Wang, K., 2011, "Class-dependent projection based method for text categorization," Pattern Recognition Letters, Vol. 32, No. 10, pp. 1493-1501.
10. Guan, H., Zhou, J., Xiao, B., Guo, M. and Yang, T., 2013, "Fast dimension reduction for document classification based on imprecise spectrum analysis," Information Sciences, Vol. 222, pp. 147-162.
11. Huang, R. and Lam, W., 2009, "An active learning framework for semi-supervised document clustering with language modeling," Data and Knowledge Engineering, Vol. 68, No. 1, pp. 49-67.
12. Zhou, S., Chen, Q. and Wang, X., 2014, "Fuzzy deep belief networks for semi-supervised sentiment classification," Neurocomputing, Vol. 131, pp. 312-322.
13. Druck, G., Pal, C., McCallum, A. and Zhu, X., 2007, "Semi-supervised classification with hybrid generative/discriminative methods," Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 280-289.
14. Yang, P., Gao, W., Tan, Q. and Wong, K.-F., 2013, "A link-bridged topic model for cross-domain document classification," Information Processing and Management, Vol. 49, No. 6, pp. 1181-1193.
15. Liu, C.-L., Hsaio, W.-H., Lee, C.-H. and Chen, C.-H., 2013, "Clustering tagged documents with labeled and unlabeled documents," Information Processing and Management, Vol. 49, No. 3, pp. 596-606.
16. Karthikeyan, M. and Aruna, P., 2013, "Probability based document clustering and image clustering using content-based image retrieval," Applied Soft Computing, Vol. 13, No. 2, pp. 959-966.
17. Zhang, W., Yoshida, T., Tang, X. and Wang, Q., 2010, "Text clustering using frequent itemsets," Knowledge-Based Systems, Vol. 23, No. 5, pp. 379-388.
18. Jun, S., Park, S.-S. and Jang, D.-S., 2014, "Document clustering method using dimension reduction and support vector clustering to overcome sparseness," Expert Systems with Applications, Vol. 41, No. 7, pp. 3204-3212.
19. Dunlavy, D. M., O’Leary, D. P., Conroy, J. M. and Schlesinger, J. D., 2007, "QCS: A system for querying, clustering and summarizing documents," Information Processing and Management, Vol. 43, No. 6, pp. 1588-1605.
20. Luo, C., Li, Y. and Chung, S. M., 2009, "Text document clustering based on neighbors," Data and Knowledge Engineering, Vol. 68, No. 11, pp. 1271-1288.
21. Akter, R. and Chung, Y., 2013, "An evolutionary approach for document clustering," IERI Procedia, Vol. 4, pp. 370-375.
22. Cagnina, L., Errecalde, M., Ingaramo, D. and Rosso, P., 2014, "An efficient Particle Swarm Optimization approach to cluster short texts," Information Sciences, Vol. 265, pp. 36-49.
23. Bsoul, Q., Salim, J. and Zakaria, L. Q., 2013, "An intelligent document clustering approach to detect crime patterns," Procedia Technology, Vol. 11, pp. 1181-1187.
24. Jameel, S. and Xiaojun, Q., 2012, "An unsupervised technical readability ranking model by building a conceptual terrain in LSI," Proceedings of the 8th International Conference on Semantics, Knowledge and Grids (SKG), pp. 39-46.
25. Yan, X., Song, D. and Li, X., 2006, "Concept-based document readability in domain specific information retrieval," Proceedings of the 15th ACM International Conference on Information and Knowledge Management, pp. 540-549.
26. Daniłowicz, C. and Baliński, J., 2001, "Document ranking based upon Markov chains," Information Processing and Management, Vol. 37, No. 4, pp. 623-637.
27. Choi, D., Kim, T., Min, M. and Lee, J.-H., 2011, "An approach to use query-related web context on document ranking," Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication, pp. 1-7.
28. Wen, K., Li, R., Xia, J. and Gu, X., 2012, "Optimizing ranking method using social annotations based on language model," Artificial Intelligence Review, Vol. 41, No. 1, pp. 81-96.
29. Elsas, J. L., Carvalho, V. R. and Carbonell, J. G., 2008, "Fast learning of document ranking functions with the committee perceptron," Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 55-64.
30. Duh, K. and Kirchhoff, K., 2011, "Semi-supervised ranking for document retrieval," Computer Speech and Language, Vol. 25, No. 2, pp. 261-281.
31. Ji, D., Zhao, S. and Xiao, G., 2009, "Chinese document re-ranking based on automatically acquired term resource," Language Resources and Evaluation, Vol. 43, No. 4, pp. 385-406.
32. Hassan, S. and Mihalcea, R., 2008, "Learning to identify educational materials," Transactions on Speech and Language Processing, Vol. 8, No. 2, pp. 1-18.
33. Bashir, S., 2014, "Estimating retrievability ranks of documents using document features," Neurocomputing, Vol. 123, pp. 216-232.
34. Paukkeri, M.-S., Ollikainen, M. and Honkela, T., 2013, "Assessing user-specific difficulty of documents," Information Processing and Management, Vol. 49, No. 1, pp. 198-212.
35. Kim, K.-J. and Cho, S.-B., 2007, "Personalized mining of web documents using link structures and fuzzy concept networks," Applied Soft Computing, Vol. 7, No. 1, pp. 398-410.
36. Herrera-Viedma, E. and Peis, E., 2003, "Evaluating the informative quality of documents in SGML format from judgements by means of fuzzy linguistic techniques based on computing with words," Information Processing and Management, Vol. 39, No. 2, pp. 233-249.
37. Liu, D.-R., Lai, C.-H. and Chiu, H., 2011, "Sequence-based trust in collaborative filtering for document recommendation," International Journal of Human-Computer Studies, Vol. 69, No. 9, pp. 587-601.
38. Qi, L., Enhong, C., Hui, X., Ding, C. H. Q. and Jian, C., 2012, "Enhancing collaborative filtering by user interest expansion via personalized ranking," IEEE Transactions on Systems, Man, and Cybernetics－Part B: Cybernetics, Vol. 42, No. 1, pp. 218-233.
39. Badi, R., Bae, S., Moore, J. M., Meintanis, K., Zacchi, A., Hsieh, H., Shipman, F. and Marshall, C. C., 2006, "Recognizing user interest and document value from reading and organizing activities in document triage," Proceedings of the 11th International Conference on Intelligent User Interfaces, pp. 218-225.
40. Hawalah, A. and Fasli, M., 2011, "A hybrid re-ranking algorithm based on ontological user profiles," Proceedings of the 3rd Conference on Computer Science and Electronic Engineering, pp. 50-55.
41. Beg, M. M. S. and Ahmad, N., 2007, "Web search enhancement by mining user actions," Information Sciences, Vol. 177, No. 23, pp. 5203-5218.
42. Akbari Torkestani, J., 2012, "An adaptive learning automata-based ranking function discovery algorithm," Journal of Intelligent Information Systems, Vol. 39, No. 2, pp. 441-459.
43. Telang, A., Li, C. and Chakravarthy, S., 2012, "One size does not fit all: Toward user- and query-dependent ranking for web databases," IEEE Transactions on Knowledge and Data Engineering, Vol. 24, No. 9, pp. 1671-1685.
44. Romão, T., Correia, N., Dias, E., Danado, J., Trabuco, A., Santos, C., Santos, R., Câmara, A. and Nobre, E., 2004, "ANTS—Augmented environments," Computers and Graphics, Vol. 28, No.5, pp. 625-633.
45. Ajanki, A., Billinghurst, M., Gamper, H., Järvenpää, T., Kandemir, M., Kaski, S., Koskela, M., Kurimo, M., Laaksonen, J., Puolamäki, K., Ruokolainen, T. and Tossavainen, T., 2010, “An augmented reality interface to contextual information,” Virtual Reality, Vol. 15, No. 2-3, pp. 161-173.
46. Julier, S., Lanzagorta, M., Baillot, Y., Rosenblum, L., Feiner, S., Hollerer, T. and Sestito, S., 2000, "Information filtering for mobile augmented reality," Proceedings of the 2000 IEEE and ACM International Symposium on Augmented Reality, pp. 3-11.
47. Xu, K., Chia, K. W. and Cheok, A. D., 2008, "Real-time camera tracking for marker-less and unprepared augmented reality environments," Image and Vision Computing, Vol. 26, No. 5, pp. 673-689.
48. Bekel, H., Heidemann, G. and Ritter, H., 2005, "Interactive image data labeling using self-organizing maps in an augmented reality scenario," Neural Networks: The Official Journal of the International Neural Network Society, Vol. 18, No.5-6, pp. 566-574.
49. Langlotz, T., Degendorfer, C., Mulloni, A., Schall, G., Reitmayr, G. and Schmalstieg, D., 2011, "Robust detection and tracking of annotations for outdoor augmented reality browsing," Computers and Graphics, Vol. 35, No. 4, pp. 831-840.
50. Castle, R., Klein, G. and Murray, D. W., 2008, "Video-rate localization in multiple maps for wearable augmented reality," Proceedings of the 12th IEEE International Symposium on Wearable Computers, pp. 15-22.
51. Minjing, P., 2012, "A model of integrating virtual and real objects based on contour algorithm and gabor filter," Procedia Engineering, Vol. 29, pp. 3949-3953.
52. Bell, B., Feiner, S. and Höllerer, T., 2001, "View management for virtual and augmented reality," Proceedings of the 14th Annual ACM Symposium on User Interface Software and Technology, pp. 101-110.
53. Seo, D. W. and Lee, J. Y., 2013, "Direct hand touchable interactions in augmented reality environments for natural and intuitive user experiences," Expert Systems with Applications, Vol. 40, No. 9, pp. 3784-3793.
54. Arbeláez-Estrada, J. C. and Osorio-Gómez, G., 2013, "Augmented reality application for product concepts evaluation," Procedia Computer Science, Vol. 25, pp. 389-398.
55. Jimeno-Morenilla, A., Sánchez-Romero, J. L. and Salas-Pérez, F., 2013, "Augmented and virtual reality techniques for footwear," Computers in Industry, Vol. 64, No. 9, pp. 1371-1382.
56. Irizarry, J., Gheisari, M., Williams, G. and Walker, B. N., 2013, "InfoSPOT: A mobile augmented reality method for accessing building information through a situation awareness approach," Automation in Construction, Vol. 33, pp. 11-23.
57. Cirulis, A. and Brigmanis, K. B., 2013, "3D outdoor augmented reality for architecture and urban planning," Procedia Computer Science, Vol. 25, pp. 71-79.
58. Novak-Marcincin, J., Barna, J., Janak, M. and Novakova-Marcincinova, L., 2013, "Augmented reality aided manufacturing," Procedia Computer Science, Vol. 25, pp. 23-31.
59. Kose, U., Koc, D. and Yucesoy, S. A., 2013, "An augmented reality based mobile software to support learning experiences in computer science courses," Procedia Computer Science, Vol. 25, pp. 370-374.
60. Horii, H. and Miyajima, Y., 2013, "Augmented reality-based support system for teaching hand-drawn mechanical drawing," Procedia - Social and Behavioral Sciences, Vol. 103, pp. 174-180.
61. Shatte, A., Holdsworth, J. and Lee, I., 2014, "Mobile augmented reality based context-aware library management system," Expert Systems with Applications, Vol. 41, No. 5, pp. 2174-2185.
62. Ginters, E. and Martin-Gutierrez, J., 2013, "Low cost augmented reality and RFID application for logistics items visualization," Procedia Computer Science, Vol. 26, pp. 3-13.
63. Benbelkacem, S., Belhocine, M., Bellarbi, A., Zenati-Henda, N. and Tadjine, M., 2013, "Augmented reality for photovoltaic pumping systems maintenance tasks," Renewable Energy, Vol. 55, pp. 428-437.
64. Kuo, C., Jeng, T. and Yang, I., 2013, "An invisible head marker tracking system for indoor mobile augmented reality," Automation in Construction, Vol. 33, pp. 104-115.
65. Lee, S., Lee, J., Lee, A., Park, N., Lee, S., Song, S., Seo, A., Lee, H., Kim, J. I. and Eom, K., 2013, "Augmented reality intravenous injection simulator based 3D medical imaging for veterinary medicine," Veterinary Journal, Vol. 196, No. 2, pp. 197-202.