隨著電腦及網路設備的快速發展與普及，電子化資訊的傳遞與儲存也逐漸的取代了一些傳統的紙上作業方式，大量的電子資訊雖然是豐富的分析資料來源，卻也是單純以人力處理的方式所無法負荷的。本論文提出一個基於資訊萃取的文件概念圖分析架構，利用自然語言處理中的詞性標記技術提供文件在語義層次的資訊，先找出文件中專有名詞類型的重要項目詞彙，再以關聯樣版將文件中的關聯資訊萃取出來，提供了一個有效的從文字性資料中發掘關聯資訊的方法。其中，從文件中萃取出來「項目→關聯描述→項目」形式的關聯資訊，也對資料集中項目的關聯狀況提供了比數值性關聯更明確的解釋。而將項目間的關聯資訊轉換後以概念圖的形式輸出，則讓使用者可以更容易的看出項目間的關聯狀況，提供了使用者一個瀏覽關聯資訊的較佳途徑。

Due to the rapid advancement and popularization of computer hardware and network equipment, traditional paper operations are replacing by electronic transmission and storage ways gradually. Although large amount of electronic information provides rich resource for analyzing, we could not accomplish these works by hand. In this thesis, we propose an Information Extraction based approach to discover relation in text documents. In our approach, we use the part-of-speech tagger to acquire semantic information in documents and begin with extracting meaningful terms from sentences according to the part-of-speech tags. Then we extract the relation information in documents by using the Relation Template. The extracted information, which is in the form of “Term→Relation Description→Term”, provides more clear comment than the relation degree for the relationships in the dataset. Further more, transforming the relation information among terms to the manner of Concept Maps gives people an easier and better way for browsing the relation information in the dataset.

[1] Agrawal, R., Imielinski T., and Swami, A.；Mining Association Rules between Sets of Items in Large Databases. Proceedings of ACM SIGMOD, pages 207-216, May 1993.
[2] Ahonen, H., Heinonen, O., Klemettinen, M., and Verkamo, A. I.；Applying Data Mining Techniques for Descriptive Phrase Extraction in Digital Document Collections. In the Proceedings of ADL’98, April 22–24, 1998 in Santa Barbara, California, USA.
[3] Bleyberg, M. Z.；Inference Rules for Text Data Mining. Technical Report CIS 99-5.
[4] Brill, E.；A simple rule-based part of speech tagger. In Proceedings of the Third Conference on Applied Natural Language Processing, ACL, Trento, Italy, 1992.
[5] Buzan, T.；The mind map book: Radiant thinking—the major evolution in human thought. London: BBC Publications, 1993.
[6] Delannoy, J. F., Feng, C., and Matwin, S.and Szpakowicz S.；Knowledge Extraction from Text: Machine Learning for Text-to-rule Translation. In Proceedings of European Conference on Machine Learning Workshop on Machine Learning and Text Analysis, pages 7--13, 1993.
[7] Feldman, R. et al.；Text Mining at the Term Level. In Proc. of the 2nd European Symposium on Principles of Data Mining and Knowledge Discovery (PKDD'98), Nantes, France, Sept 1998.
[8] Fisher, K.M., Faletti, J., and Quinn, C.N.；Exploring cognitive learning with semantic networks. CRMSE, San Diego State University, 1990.
[9] Fisher, K. M.；Semantic networking: The new kid on the block. Journal
of Research in Science Teaching, 27 (10), 1001-1018, 1990.
[10] Fisher, K. M., Faletti, J., Patterson. H. A., Thornton, R., Lipson, J., and Spring, C.；Computer-based concept mapping: SemNet software - a tool for describing knowledge networks. Journal of College Science Teaching , 19 (6), 347-352, 1990.
[11] Hann, U. and Schnattinger, K.；Knowledge Mining from Textual Sources. In CIKM`97 - Proceedings of the 6th Intl. Conference on Information and Knowledge Management, pp. 83-90, 1997.
[12] Jonassen, D.H., Reeves, T., and Hong, N.；Concept mapping as cognitive learning and assessment tools. Journal of Interactive Learning Research, 8, 289–308, 1998.
[13] McAleese, R.；A theoretical view on concept mapping. ALT-J, 2, 1, 38–48, 1994.
[14] McAleese, R.；Journal of Interactive Learning Research, 8, 314 (221 pp.).Special double issue—Concept mapping—book review, 1999.
[15] McAleese, R., Grabinger, S., and Fisher, K.；The knowledge arena: A learning environment that underpins concept mapping. AERA Conference paper, Montreal, April 18–25, 1999.
[16] McAleese, R.；The Knowledge Arena as an Extension to the Concept Map:Reflection in Action. Interactive Learning Environments, 1998, Vol. 6, No. x, pp. 1–22
[17] Moldovan, D. and Girju R.；Domain-Specific Knowledge Acquisition and Classification using WordNet. In Proceedings of FLAIRS-2000 conference, Orlando, Fl., May 2000.
[18] Nahm, U. Y. and Mooney, R. J.；Text Mining with Information Extraction. In Proceedings of the AAAI 2002 Spring Symposium on Mining Answers from Texts and Knowledge Bases, pages 60-67, Stanford, CA, March 2002.
[19] Nahm, U. Y. and Mooney, R. J.；Using information extraction to aid the discovery of prediction rules from texts. In Proceedings of the Sixth International Conference on Knowledge Discovery and Data Mining (KDD-2000) Workshop on Text Mining, pages 51--58, Boston, MA, August 2000.
[20] Nahm, U. Y. and Mooney, R. J.；Mining Soft Matching Rules from Textual Data. In Proceedings of the Seventeenth International Joint Conference on Articial Intelligence (IJCAI-2001), Seattle, WA, 2001.
[21] Pak Chung Wong, Whitney, P., and Thomas, J.；Visualizing Association Rules for Text Mining. In Graham Wills and Daniel Keim, editors, Proceedings of IEEE Information Visualization '99, Los Alamitos, CA, 1999.
[22] Rajman, M.；Text Mining - Knowledge extraction from unstructured textual data. In Proc. of EUROSTAT Conference, Francfort (Deutchland), may, 1997.