隨著科技技術快速發展、數位資訊大幅膨脹，資訊文件之類型亦日漸繁雜且多樣化，以致讀者不易於短時間內大量吸收。因此，為使讀者快速且準確地吸收其所需資訊與知識，良好的知識呈現方式乃成為現今資訊服務與知識管理之重要課題。在一般知識分享環境中，傳統以文字形式為主之知識表達形式易造成新進人員難以迅速吸收大量專業知識；而關聯性動作之相關知識因具有更多較為重要且操作較易混淆之句子，造成知識需求者往往需花費大量時間加以閱讀、理解。而因圖形化之知識表達方式可提升知識需求者學習速率與維持記憶之長久性等效果，本研究將針對關聯性動作相關知識，提出一套將關聯性動作相關知識制式化與視覺化之方法論，並根據該方法論建構一套可自動將文字資訊以視覺化方式動態呈現之系統。
本方法論之精神乃由自由形式知識文件中取得關聯性動作相關之文句，並將該等文句以標準化之制式形式表示，最後再將各制式形式文句以視覺化方式動態呈現。本方法論之詳細作法首先乃自動擷取自由形式文件中之新詞彙，並將其新增至詞庫中，以確保詞庫完整度。確保詞庫之完整性後，則將自由形式知識文件中各文句內容與關聯性動作詞庫進行比對，以取得隱含關聯性動作詞彙之目標文句，同時將該文句之關鍵詞彙予以解析並存入制式矩陣，以標準文句形式表示之。之後，將各制式矩陣中之關鍵詞彙與圖形庫進行比對，以取得對應之圖形與資訊，再以虛擬實境模組語言（VRML）呈現文句內容所對應之動態視覺效果。
最後，本研究更依據所提之方法論建構一套動態知識視覺呈現系統並以「電腦組裝」領域之文件資料進行案例驗證，以確認方法論與技術之可行性；而由驗證結果可知，本系統僅需一定數量之訓練資料即可達良好之績效。整體而言，本研究所提出之知識制式化與視覺化之方法將有助於知識需求者將大量資訊與知識快速且準確吸收，可大幅提升企業進行教育訓練與知識再運用之效率與效能。

Due to the rapid growth of information technology, digital information has significantly increased over the Internet. The growing complexity of information and documents has made it hard for knowledge receivers to efficiently and accurately to recognize the digital contents. Therefore, an appropriate knowledge representation scheme is required for enterprise knowledge management and services. Traditional schemes for explicit knowledge representation within the enterprise and academic circles are mostly text-oriented and as a result, much time and efforts are required for knowledge receivers to recognize the knowledge contents, especially for the motion knowledge.
In this research, a three-phase methodology (including automatic thesaurus definition (ATD), target sentence extraction and formatting (TSEF) and motion knowledge visualization (MKV)) for motion knowledge extraction, representation and visualization is developed. Moreover, based on the proposed methodology, a Motion Knowledge Representation and Management System (MKRMS) is established, and a “Computer Assembly” case is applied in order to verify the feasibility of the proposed model. The verification results show that the system could achieve a well performance with simply a small amount of training data.
As a whole, this research provides a knowledge representation and visualization approach to facilitate knowledge receivers to efficiently and accurately acquire the knowledge contents. The proposed methodology can be applied in enterprise e-training and knowledge management systems to enhance reuse of domain knowledge.

1. 陳彥仲，2003，「使用Ontology在特定領域為資訊作自動化的標記」，碩士論文（指導教授：孫春在），國立交通大學資訊科學研究所。
2. 潘智仁，2003，「從非結構化文件中學習資訊粹取法則之技術」，碩士論文（指導教授：魏志平），國立中山大學資訊管理研究所。
3. Ahmad, K., Vrusias, B. and Zhu, M., 2005, “Visualising an image collection?” Proceedings of the 9th International Conference on Information Visualisation, pp. 268-274.
4. Allan, K., 1980, “Nouns and countability,” Journal on Linguistic Society of America, Vol. 56, pp. 541-567.
5. Appelt, D. E., Hobbs, J. R., Bear, J., Israel, D. and Tyson, M., 1993, “FASTUS: A finite-state processor for information extraction from real-world text,” Proceedings of the 13th International Joint Conference on Artificial Intelligence, pp. 1172-1178.
6. Baldwin, T. and Bond, F., 2003, “A plethora of methods of learning English countability,” Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, pp. 73-80.
7. Baldwin, T. and Bond, F., 2003, “Learning the countability of English nouns from corpus data,” Proceedings of the 41st Annual Meeting of the Association of Computational Linguistics, pp. 463-470.
8. Boughanem, M., Layaida, R. and Caron, A., 1993, “A neural network model for documentary base self-organising and querying,” Proceedings of the 5th International Conference on Computing and Information, pp. 512-518.
9. Brown, M. E. and Gallimore, J. J., 1995, “Visualization of three-dimensional structure during computer-aided design,” International Journal of Human: Computer Interaction, Vol. 7, pp. 37-56.
10. Cammelli, A. and Socci, F., 1998, “A thesaurus for improving information retrieval in an integrated legal expert system,” Proceedings of the 9th International Workshop on Database and Expert Systems Applications, pp. 619-624.
11. Card, S. and Mackinlay, J., 1997, “The structure of the information visualization design space,” IEEE Computer Society Press, pp.92-101.
12. Chen, A., He, J., Xu, L., Gey, F. C. and Meggs, J., 1997, “Chinese text retrieval without using a dictionary,” Proceedings of the 20th Annual International ACM SIGIR Conference, Vol. 31, pp. 42-49.
13. Chen, C. and Chen, H. S., 2005, “Integrating punctuation rules and naive Bayesian model for Chinese creation title recognition,” Proceedings of the 2nd International Joint Conference on Natural Language Processing, pp. 839-848.
14. Chen, H., Lynch, K. J., Basu, K. and Ng, T. D., 1993, “Generating, integrating, and activating thesauri for concept-based document retrieval,” IEEE Expert: Intelligent Systems and Their Applications, pp. 25-24.
15. Chen, K. H. and Chen, H. H., 1993, “A probabilistic chunker,” Proceedings of the 6th R.O.C. Computational Linguistics Conference, pp. 99-117.
16. Chien, L., 1999, “PAT-tree-based adaptive key phrase extraction for intelligent Chinese information retrieval,” Information Processing and Management, Vol. 35, pp. 501-521.
17. Chittaro, L., 2001, “Information visualization and its application to medicine,” Artificial Intelligence in Medicine, Vol. 22, pp. 81-88.
18. Cowie, J. and Lehnert, W., 1996, “Information extraction,” Communications of the ACM, Vol. 39, pp. 80-91.
19. Ezquerra, N., Braal, L., Garcia, E., Cookeb, C. and Krawczynska, E., 1999, “Interactive, knowledge-guided visualization of 3D medical imagery,” Future Generation Computer Systems, Vol. 15, pp. 59-73.
20. Gallimore, J. J. and Brown, M. E., 1993, “Visualization of 3-D computer-aided design objects,” International Journal of Human: Computer Interaction, Vol. 5, pp. 361-382.
21. Gao, J., Li, M., Wu, A. and Huang, C., 2005, “Chinese word segmentation and named entity recognition: A pragmatic approach,” Computational Linguistics, Vol. 31, pp. 531-574.
22. Ge, X., Pratt, W. and Smyth, P., 1999, “Discovering Chinese words from unsegmented text,” Research and Development in Information Retrieval, Vol. 2, pp. 271-272.
23. Gershon, N. and Eick, S. G., 1995, “Visualization's new tack: Making sense of information,” Spectrum, IEEE, Vol. 32, pp.38-40, 42, 44-7, 55-6.
24. Gershon, N., Eick, S. G. and Card, S., 1998, “Information visualization,” ACM Interactions, Vol. 5, pp.9-15.
25. Goh, C., Asahara, M. and Matsumoto, Y., 2005, “Chinese word segmentation by classification of characters,” Computational Linguistics and Chinese Language Processing, Vol. 10, pp. 381-396.
26. Heiko, G., 2001, “On parsing and condensing substrings of LR languages in linear time,” Theoretical Computer Science, Vol. 267, No. 1-2, pp. 61-82.
27. Hessler, D., Young, S. J. and Ellisman, M. H., 1996, “A flexible environment for the visualization of three-dimensional biological structures,” Journal of Structural Biology, Vol. 116, pp. 113-119.
28. Hindle, D., 1983, User Manual for Fidditch, A Deterministic Parser, Naval Research Laboratory Technical Memorandum 7590-142, Naval Research Laboratory, Washington, D.C..
29. Ho, E. K. S. and Chen, L. W., 2002, “Extracting error productions from a neural network-based LR parser,” Neurocomputing, Vol. 47, No. 1-4, pp. 189-206.
30. Hodge, V. J. and Austin, J., 2002, “Hierarchical word clustering - Automatic thesaurus generation,” Neurocomputing, Vol. 48, No. 1, pp. 819-846.
31. Horng, J. and Yeh, C., 2000, “Applying genetic algorithms to query optimization in document retrieval,” Information Processing and Management, Vol. 36, pp. 737-759.
32. Hou, J. L., Yu, F. J. and Lin, R. K., 2006, "A knowledge component analysis model based on term frequency and correlation analysis," Journal of Computer Information Systems, Vol. 46, No. 4, pp. 64-77.
33. Huang, C., Chen, K., Chen, F. and Chang, L., 1997, “Segmentation standard for Chinese natural language processing,” Computational Linguistics and Chinese Language Processing, Vol. 2, pp. 47-62.
34. Hubona, G. S., Shirah, G. W. and Four, D.G., 1997, “The effects of motion and stereopsis on three-dimensional visualization,” International Journal of Human-Computer Studies, Vol. 47, No. 5, pp. 609-627.
35. Hundhausen C. D., 2005, “Using end-user visualization environments to mediate conversations: A ‘Communicative Dimensions’ framework,” Journal of Visual Languages and Computing, Vol. 16, pp. 153-185.
36. IBM-FeatureExtraction,
http://www-3.ibm.com/software/data/iminer/fortext/extract/extract.html.
37. Ibrahim, B., 1998, “Visual navigation: Methods and tools,” Journal of Visual Languages & Computing, Vol. 9, pp.263-265.
38. Karavanic, K. L., Myllymaki, J., Livny, M. and Miller, B. P., 1997, “Integrated visualization of parallel program,” Parallel computing, Vol. 23, pp. 181-198.
39. Kilgarriff, A., 2003, “ Thesauruses for natural language processing,” Proceedings of the 2003 International Conference on Natural Language Processing and Knowledge Engineering, pp. 5-13.
40. Lane, P. C. R. and Henderson, J. B., 2001, “ Incremental syntactic parsing of natural language corpora with simple synchrony networks,” IEEE Transactions on Knowledge and Data Engineering, Vol. 13, No. 2, pp. 219-231.
41. Law H. and Chan, C., 1996, “N-th order Ergodic multigram HMM for modeling of languages without marked word boundaries,” International Conference on Computational Linguistics, Vol. 1, pp. 204-209.
42. Lim, E. and Honjo, T., 2003, “Three-dimensional visualization forest of landscapes by VRML,” Landscape and Urban Planning, Vol. 63, pp. 175-186.
43. Liu, Y. and Wickens, C. D., 1992, “Use of computer graphics and cluster analysis in aiding relational judgement,” Human Factors, Vol. 34, pp. 165-178.
44. Manco, G., Pizzuti, C. and Talia, D., 2004, “Eureka!: An interactive and visual knowledge discovery tool,” Journal of Visual Languages & Computing, Vol. 15, pp. 1-35.
45. Mayberry, A. R., Crocker M. W. and Knoeferle, P., 2005, “A connectionist model of anticipation in visual worlds,” Proceedings of the 2nd International Joint Conference on Natural Language Processing, pp. 849-861.
46. Merriam Webster Online Dictionary, 2006, http://www.m-w.com.
47. Message Understanding Conferences, http://www.muc.saic.com.
48. Morris, S., DeYong, C., Wu, Z., Salman, S. and Yemenu, D., 2002, “Diva: A visualization system for exploring document databases for technology forecasting,” Computers and Industrial Engineering, Vol. 43, pp. 841-862.
49. MUC7, 1998, Proceedings of 7th Message Understanding Conference, Fairfax, VA.
50. Nagata, R., Wakana, T. and Masui, F., 2005, “Detecting article errors based on the mass count distinction,” Proceedings of the 2nd International Joint Conference on Natural Language Processing, pp. 815-826.
51. Neumann, G., 1998, “Interleaving natural language parsing and generation through uniform processing,” Artificial Intelligence Volume, Vol. 99, pp. 121-163.
52. Nie, J. and Ren, F., 1999, “Chinese information retrieval: Using characters or words?” Information Processing and Management, Vol. 35, pp. 443-462.
53. O’Hara, T., 2003, “Inducing criteria for mass noun lexical mappings using the Cyc KB, and its extension to WordNet,” Communications of the ACM, Vol.38, pp. 425-441.
54. Owei, V., 2000, “Natural language querying of databases: An information extraction approach in the conceptual query language,” International Journal of Human-Computer Studies, Vol. 53, pp. 439-492.
55. Park, Y. C. and Key-Sun, C., 1996, “Automatic thesaurus construction using Bayesian networks,” Information Processing & Management, Vol. 32, No. 5, pp. 543-553.
56. Peng, F., 1999, “Models development in IE tasks,” A survey: CS685 (Intelligent Computer Interface) course project, Computer Science Department, University of Waterloo.
57. Perez-Carballo, J. and Strzalkowski, T., 2000, “Natural language information retrieval: Progress report,” Information Processing & Management, Vol. 36, pp. 155-178.
58. Proffitt, D. R. and Kaiser, M. K., 1991, “Perceiving environmental properties from motion information: minimal conditions,” Pictorial Communication in Virtual and Real Environments, Vol. 4, pp. 47-60.
59. Riloff, E. and Lehnert, W., 1994, “Information extraction as a basis for high-precision text classification,” ACM Transactions on Information Systems, Vol. 12, No. 3, pp. 296-333.
60. Sahami, M., Dumais, S., Heckerman, D. and Horvitx, E., 1998, “A Bayesian approach to filtering junk e-mail,” Learning for Text Categorization: Papers from the AAAI Workshop, pp. 55-62.
61. Sarathy, S., Shujaee, K. and Cannon, K., 2000, “Visualization of large complex datasets using virtual reality,” Information Technology: Coding and Computing, pp.522-526.
62. Shepard, R. N. and Metzler, J., 1971, “Mental rotation of three-dimensional objects,” Science, Vol. 171, pp.701-703.
63. Shiotani, N. and Miyamoto, S., 1997, “Image retrieval system using an iconic thesaurus,” IEEE International Conference on Intelligent Processing Systems, Vol. 1, pp. 44-48.
64. Shishibori, M., Lee, S. S., Oono, M. and Aoe, J., 2002, “Improvement of the LR parsing table and its application to grammatical error correction,” Information Sciences, Vol. 18, No. 1-4, pp. 11-26.
65. Sollenberger, R. L. and Milgram, P., 1993, “Effects of stereoscopic and rotational displays in three-dimensional path-tracing task,” Human Factors, Vol. 35, pp. 483-499.
66. Steimann, F., 1998, “Dependency parsing for medical language and concept representation,” Artificial Intelligence in Medicine, Vol. 12, pp. 77-86.
67. Strzalkowski, T., 1995, “Natural language information retrieval,” Information Processing & Management, Vol. 31, pp. 397-417.
68. Syu, I., Lang, S. D. and Deo, N., 1996, “A neural network model for information retrieval using latent semantic indexing,” IEEE International Conference on Neural Networks, Vol. 2, pp. 1318-1323.
69. Thurmond, J. B., Drzewiecki, P. A. and Xu, X., 2005, “Building simple multiscale visualizations of outcrop geology using virtual reality modeling language (VRML),” Computers and Geosciences, Vol. 31, pp. 913-919.
70. Van Damme, W. J. M. and Van de Grind, W. A., 1993, “Active vision and the identification of three-dimensional shape,” Vision Research, Vol. 11, pp. 1581-1587.
71. Ware, C. and Franck, G., 1996, “Evaluating stereo and motion cues for visualizing information nets in three dimensions,” ACM Transactions on Graphics, Vol. 15, pp. 121-140.
72. Wasfy, H. M., Wasfy, T. M. and Noor, A. K., 2004, “An interrogative visualization environment for large-scale engineering simulations,” Advances in Engineering Software, Vol. 35, pp. 805-813.
73. Wesson, J. L., 2005, “Applying visualization techniques in novel domains,” Information Visualization, Vol. 9, pp. 619-625.
74. Wikipedia, 2006, http://en.wikipedia.org/wiki/Parsing.
75. Wikipedia, 2006,
http://en.wikipedia.org/w/index.php?title=Special%3ASearch&search=thesuarus&fulltext=Search.
76. Wilks, Y., 1997, “Information extraction as a core language technology,” Recent Advances in Natural Language Processing, Vol. 7, pp. 1-10.
77. Xiao, J., Liu, B. and Wang, X., 2005, “Principles of non-stationary hidden Markov model and its applications to sequence labeling task,” Proceedings of the 2nd International Joint Conference on Natural Language Processing, pp. 826-837.
78. Xue, N., 2003, “Chinese word segmentation as character tagging,” Computational Linguistics and Wilks Chinese Language Processing, Vol. 8, pp. 29-48.
79. Xue, K., Cheng, J., He, P. and Zheng, J., 1999, “A 3D visualization tool for landmark allocation,” Engineering in Medicine and Biology, Vol. 1, pp. 651.
80. Zhang, J., 2001, “TOFIR: A tool of facilitating information retrieval-introduce a visual retrieval model,” Information Processing and Management, Vol. 37, pp. 639-657.
81. Zhang, M., Lu, Z. and Zou, C., 2004, “A Chinese word segmentation based on language situation in processing ambiguous words,” Information Sciences, Vol. 162, pp. 275-285.
82. Zlodi, G. and Maroevic, I., 2003, “Thesaurus as a tool for image indexing and retrieval in the field of cultural heritage,” Proceedings of the 4th EURASIP Conference focused on Video/Image Processing and Multimedia Communications, Vol. 2, pp. 513-518.