當維修人員欲理解一損壞零件之維修方式時，其往往透過各類管道蒐集相關維修技術文件，並自此些技術文件中挑選合適的維修技術修復該損壞零件。然而，維修技術文件之內容常以文字方式描述維修過程，且部分零件之構造繁複，造成維修人員不易依維修技術文件之文字內容修理損壞零件；因此，維修人員需耗費額外時間透過各類管道取得此些不易理解之文字內容的詳細資訊，以修復一損壞零件。
為解決上述問題，本研究乃提出「輔助維修人員維修之技術文件內容視覺化」模式，而此模式包含「維修技術內容解析」前置階段、「維修技術內容圖示化整合及視覺化」三階段方法論。具體而言，此方法論先透過「維修技術特徵屬性擷取及合併」階段將維修技術內容進行維修技術之特徵屬性擷取及合併；接著，依關鍵屬性值與相關維修網頁內容之比對結果，「關鍵維修技術屬性值圖示化」階段乃計算維修網頁中圖片對應關鍵屬性值之資訊含量，以擷取可代表關鍵屬性值之圖片；最後，「維修技術屬性值圖示整合及維修技術視覺化」階段將可代表關鍵屬性值之圖片和無圖示化之屬性值進行整合，並以視覺化方式呈現維修技術，進而使維修人員有效率地修復一損壞零件。

Once a maintenance technician wants to realize how to repair a damaged part, he/she often collects related technical documents in order to acquire the appropriate maintenance techniques to repair the damage. The content in the technical documents is often represented in text format to express the detailed maintenance processes. In addition, the structure of some parts might be complicated. Therefore, the maintenance technician has to spend time in comprehending the maintenance processes by using distinct approaches to repair the damaged part.
In order to solve the above problems, this research develops a model for a maintenance technician. The proposed model can be used to acquire pictures to represent the key characteristics of a technical report for maintenance technology in order to visually illustrate the critical contents in the technical report. By using the model, the maintenance technician can effectively comprehend the maintenance technology for efficient repair of the damaged part.

摘要...I
ABSTRACT...II
目錄...III
圖目錄...V
表目錄...VII
第一章、研究背景...1
1.1研究動機與目的...1
1.2研究步驟...5
1.3研究定位...8
第二章、文獻回顧...11
2.1技術文件之特徵屬性擷取...11
2.1.1依量化特性擷取技術文件之特徵屬性...11
2.1.2依質化特性擷取技術文件之特徵屬性...17
2.1.3依質化與量化特性擷取技術文件之特徵屬性...24
2.2以內容為基礎之圖片擷取...28
2.2.1依文字內容進行圖片擷取...29
2.2.2依文字內容及圖片內容進行圖片擷取...34
2.3技術文件視覺化...41
2.3.1呈現技術文件間之關係的視覺化方法...41
2.3.2呈現技術文件中內容之視覺化方法...46
2.3.3呈現技術文件中內容及文件間之關係的視覺化方法...51
2.4小結...56
第三章、輔助維修人員維修之技術文件內容視覺化模式...58
3.1維修技術內容解析...60
3.1.1維修技術內容特徵屬性釐清...61
3.1.2維修技術內容特徵屬性表達結構釐清...70
3.2維修技術特徵屬性擷取及合併...80
3.3維修技術關鍵屬性值圖示化...100
3.4維修技術屬性值圖示整合及維修技術視覺化...122
3.5小節...127
第四章、績效驗證與分析...128
4.1績效驗證規劃...128
4.2驗證結果分析...132
第五章、結論與未來展望...163
5.1論文總結...163
5.2未來展望...166
參考文獻...168
附錄A、模式驗證資料...174
附錄B、模式於第二階段績效驗證結果...202
附錄C、「圖片與關鍵屬性值之相關程度判定」驗證議題之問卷...236

1. Adrian, I. and Baboi, A.-M., 2016, “Using Semantic Resources in Image Retrieval,” Procedia Computer Science, Vol. 96, pp. 436-445.
2. Bartolini, I. and Ciaccia, P., 2007, “Imagination: Exploiting Link Analysis for Accurate Image Annotation,” Adaptive Multimedia Retrieval: Retrieval, User, and Semantics, pp. 32-44.
3. Bergmann, I., Butzke, D., Walter, L., Fuerste, J.-P., Moehrle, M.-G. and Erdmann, V.-A., 2008, “Evaluating the Risk of Patent Infringement by Means of Semantic Patent Analysis: The Case of DNA Chips,” R&D Management, Vol. 38, pp. 550-562.
4. Cascia, M.-L., Sethi, S. and Sclaroff, S., 1998, “Combining Textual and Visual Cues for Content-Based Image Retrieval on the World Wide Web,” Proceedings of IEEE Workshop on Content-Based Access of Image and Video Libraries, pp. 24-28.
5. Chang, P.-L., Wu, C.-C. and Leu, H.-J., 2012, “Investigation of Technological Trends in Flexible Display Fabrication through Patent Analysis,” Displays, Vol. 33, pp. 68-73.
6. Choi, C. and Park, Y., 2009, “Monitoring the Organic Structure of Technology Based on the Patent Development Paths,” Technological Forecasting and Social Change, Vol. 76, pp. 754-768.
7. Choi, J. and Hwang, Y.-S., 2014, “Patent Keyword Network Analysis for Improving Technology Development Efficiency,” Technological Forecasting and Social Change, Vol. 83, pp. 170-182.
8. Ding, G., Wang, J., Xu, N. and Zhang, L., 2009, “Automatic Image Annotations by Mining Web Image Data,” 2009 IEEE International Conference on Data Mining Workshops, pp. 152-157.
9. Dowdall, J., Hess, M., Kahusk, N., Kaljurand, K., Koit, M., Rinaldi, F. and Vider, K., 2002, “Technical Terminology as a Critical Resource,” Proceedings of International Conference on Language Resources and Evaluation, pp. 1897-1903.
10. Guo, K., Zhang, R., Zhou, Z., Tang, Y. and Kuang, L., 2016, “Combined Retrieval: A Convenient and Precise Approach for Internet Image Retrieval,” Information Sciences, Vol. 358, pp. 151-163.
11. Hou, J.-L., Chuo, H.-C. and Sun, M.-T., 2004, “Heuristic and Integrated Approach for Technical Document Authority and Authentication Sequence Determination,” International Journal of Production Research, Vol. 42, No. 9, pp. 1747-1768.
12. Hua, Z., Wang, X.-J., Liu, Q. and Lu, H., 2005, “Semantic Knowledge Extraction and Annotation for Web Images,” Proceedings of the 13th Annual ACM International Conference on Multimedia, pp. 467-470.
13. Jeon, J., Lee, C. and Park, Y., 2011, “How to Use Patent Information to Search Potential Technology Partners in. Open Innovation,” Journal of Intellectual Property Rights, Vol. 16, pp. 385-393.
14. Jin, Y., Khan, L., Wang, L. and Awad, M., 2005, “Image Annotations by Combining Multiple Evidence & Wordnet,” Proceedings of the 13th annual ACM international conference on Multimedia, pp. 706-715.
15. Joung, J. and Kim, K., 2016, “Monitoring Emerging Technologies for Technology Planning Using Technical Keyword Based Analysis from Patent Data,” Technological Forecasting and Social Change, Vol. 114, pp. 281-292.
16. Jun, S., Park, S.-S. and Jang, D.-S., 2012, “Technology Forecasting Using Matrix Map and Patent Clustering,” Industrial Management & Data Systems, Vol. 112, No. 5, pp. 786-807.
17. Kim, M., Park, Y. and Yoon, J., 2016, “Generating Patent Development Maps for Technology Monitoring Using Semantic Patent-Topic Analysis,” Computers & Industrial Engineering, Vol. 98, pp. 289-299.
18. Kim, W.-C., Song, J.-Y., Kim, S.-W. and Park, Sanghyun., 2008, “Image Retrieval Model Based on Weighted Visual Features Determined by Relevance Feedback,” Information Sciences, Vol. 178, pp. 4301-4313.
19. Kim, Y.-G., Suh, J.-H. and Park, S.-C., 2008, “Visualization of Patent Analysis for Emerging Technology,” Expert Systems with Applications, Vol. 34, pp. 1804-1812.
20. Lai, K.-K. and Wu, S.-J., 2005, “Using the Patent Co-Citation Approach to Establish a New Patent Classification System,” Information Processing & Management, Vol. 41, pp. 313-330.
21. Lee, C., Jeon, J. and Park, Y., 2011, “Monitoring Trends of Technological Changes Based on the Dynamic Patent Lattice: A Modified Formal Concept Analysis Approach,” Technological Forecasting and Social Change, Vol. 78, pp. 690-702.
22. Lee, C., Kang, B. and Shin, J., 2015, “Novelty-Focused Patent Mapping for Technology Opportunity Analysis,” Technological Forecasting and Social Change, Vol. 90, pp. 355-365.
23. Lee, M. and Lee, S., 2017, “Identifying New Business Opportunities from Competitor Intelligence: An Integrated Use of Patent and Trademark Databases,” Technological Forecasting and Social Change, Vol. 119, pp. 170-183.
24. Lee, S., Lee, S., Seol, H. and Park, Y., 2008, “Using Patent Information for Designing New Product and Technology: Keyword Based Technology Roadmapping,” R&D Management, Vol. 38, pp. 169-188.
25. Li, X., Snoek, C.-G.-M. and Worring, M., 2008, “Learning Tag Relevance by Neighbor Voting for Social Image Retrieval,” Proceedings of the ACM International Conference on Multimedia Information Retrieval, pp. 180-187.
26. Li, Y.-R., Wang, L.-H. and Hong, C.-F., 2009, “Extracting the Significant-Rare Keywords for Patent Analysis,” Expert Systems with Applications, Vol. 36, pp. 5200-5204.
27. Liu, D., Hua, X.-S., Yang, L., Wang, M. and Zhang, H.-J., 2009, “Tag Ranking,” Proceedings of the 18th International Conference on World Wide Web Pages, pp. 351-360.
28. Liu, Z. and Zhu, D., 2009, “Web Mining Based Patent Analysis and Citation Visualization,” Web Mining and Web-Based Application, pp. 19-23.
29. Lopez, P. and Romary, L., 2010, “HUMB: Automatic Key Term Extraction from Scientific Articles in GROBID,” Proceedings of the 5th International Workshop on Semantic Evaluation, pp. 248-251.
30. Lu, Z. and Wang, L., 2015, “Learning Descriptive Visual Representation by Semantic Regularized Matrix Factorization,” Pattern Recognition, Vol. 48, pp. 498-508.
31. Niemann, H., Moehrle, M.-G. and Frischkorn, J., 2017, “Use of a New Patent Text-Mining and Visualization Method for Identifying Patenting Patterns over Time: Concept, Method and Test Application,” Technological Forecasting and Social Change, Vol. 115, pp. 210-220.
32. Park, S., Lee, S.-J. and Jun, S., 2015, “A Network Analysis Model for Selecting Sustainable Technology,” Sustainability, Vol. 7, pp. 13126-13141.
33. Park, S.-S., Jung, W.-G., Shin, Y.G. and Jang, D.-S., 2011, “A Patent Examining Expert System Using Pattern Recognition,” Expert Systems with Applications, Vol. 38, pp. 4302-4311.
34. Rinaldi, F., Dowdall, J., Hess, M., Mollá, D., Schwitter, R. and Kaljurand, K., 2003, “Knowledge-Based Question Answering,” Proceedings of International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, pp. 785-792.
35. Rui, X., Yu, N., Wang, T. and Li, M., 2007, “A Search-Based Web Image Annotation Method,” 2007 IEEE International Conference on Multimedia and Expo, pp. 655-658.
36. Seo, W., Yoon, J., Park, H., Coh, B.-Y., Lee, J.-M. and Kwon, O.-J., 2016, “Product Opportunity Identification Based on Internal Capabilities Using Text Mining and Association Rule Mining,” Technological Forecasting & Social Change, Vol. 105, pp. 94-104.
37. Seol, H., Lee, S. and Kin, C., 2011, “Identifying New Business Areas Using Patent Information: A DEA and Text Mining Approach,” Expert Systems with Applications, Vol. 38, pp. 2933-2941.
38. Shen, H.-T., Ooi, B.-C. and Tan, K.-L., 2000, “Giving Meanings to WWW Images,” Proceedings of the Eighth ACM International Conference on Multimedia, pp. 39-47.
39. Son, C., Suh, Y., Jeon, J. and Park, Y., 2012, “Development of A GTM-Based Patent Map for Identifying Patent Vacuums,” Expert Systems with Applications, Vol. 39, pp. 2489-2500.
40. Song, K., Kim, K.-S. and Lee, S., 2017, “Discovering New Technology Opportunities Based on Patents: Text-Mining and F-Term Analysis,” Technovation, Vol. 60-61, pp. 1-14.
41. Srinivasarao, V. and Varma, V., 2012, “Web Image Annotation Using an Effective Term Weighting,” International Conference on Computational Linguistics and Intelligent Text Processing, Vol. 2, pp. 286-296.
42. Trappey, A.-J.-C. and Trappey, C.-V., 2008, “An R&D Knowledge Management Method for Patent Document Summarization,” Industrial Management & Data System, Vol. 108, pp. 245-257.
43. Trappey, A.-J.-C., Trappey, C.-V., Wu, C.-Y. and Lin, C.-W., 2012, “A Patent Quality Analysis for Innovative Technology and Product Development,” Advanced Engineering Informatics, Vol. 26, pp. 26-34.
44. Trappey, C.-V., Wu, H.-Y., Fataneh, T.-D. and Trappey, A.-J.-C., 2011, “Using Patent Data for Technology Forecasting: China RFID Patent Analysis,” Advanced Engineering Informatics, Vol. 25, pp. 53-64.
45. Tseng, V-S., Su, J.-H., Wang, B.-W. and Lin, Y.-M., 2007, “Web Image Annotation by Fusing Visual Features and Textual Information,” Proceedings of the 2007 ACM Symposium on Applied Computing, pp. 1056-1060.
46. Wang, C., Jing, F., Zhang, L. and Zhang, H.-J., 2006, “Image Annotation Refinement Using Random Walk with Restarts,” Proceedings of the 14th ACM International Conference on Multimedia, pp. 647-650.
47. Wu, L., Jin, R. and Jain, A.-K., 2013, “Tag Completion for Image Retrieval,” Proceedings of IEEE International Conference on Pattern Analysis and Machine Intelligence, Vol. 35, pp. 716-727.
48. Yoon, B. and Park, Y., 2004, “A Text-Mining-Based Patent Network: Analytical Tool for High-Technology Trend,” Journal of High Technology Management Research, Vol. 15, pp. 37-50.
49. Yoon, B.-U., Yoon, C.-B. and Park, Y.-T., 2002, “On the Development and Application of a Self-Organizing Feature Map-Based Patent Map,” R&D Management, Vol. 32, pp. 291-300.
50. Yoon, J. and Kim, K., 2011, “Identifying Rapidly Evolving Technological Trends for R&D Planning Using SAO-Based Semantic Patent Networks,” Scientometrics, Vol. 88, pp. 213-228.
51. Yoon, J. and Kim, K., 2012, “An Analysis of Property-Function Based Patent Networks for Strategic R&D Planning in Fast-Moving Industries: The Case of Silicon-Based Thin Film Solar Cells,” Expert Systems with Applications, Vol. 39, pp. 7709-7717.
52. Yoon, J. and Kim, K., 2012, “Detecting Signals of New Technological Opportunities Using Semantic Patent Analysis and Outlier Detection,” Scientometrics, Vol. 90, pp. 445-461.
53. Yoon, J., Park, H. and Kim, K., 2013, “Identifying Technological Competition Trends for R&D Planning Using Dynamic Patent Maps: SAO-Based Content Analysis,” Scientometrics, Vol. 94, pp. 313-331.
54. Yoon, J., Seo, W., Coh, B.-Y., Song, I. and Lee, J.-M., 2017, “Identifying Product Opportunities Using Collaborative Filtering-Based Patent Analysis,” Computers & Industrial Engineering, Vol. 107, pp. 376-387.