The fast evolution of technology has made an intensive competition environment, the company which can update information rapidly and precisely will take the competitive advantage. However, the information explosion made us have to read all the information. It is time consuming to keep information from various sources updated, and could potentially delay the reaction to the changing technology and business environments.
This thesis proposes a technology monitoring service to monitor the evolution of business ecosystem through extracting relations from various sources of information automatically. In order to construct business ecosystem that consist of companies, products and technologies. This study enhances the relation extraction methodology to extract mutual interaction from multi name entity types.
The service of the proposed system helps companies forecast the technology trends and market status through business ecosystem and other information extracted through text mining. To reduce the monitoring cost of companies, the system can extract information that companies interested in automatically and effectively catch the information related to the evolution of business ecosystem in real time.

因為科技快速的演進，我們已經面臨一個超競爭的環境，如果公司能快速且準確的掌握市場訊息，就可以取得競爭優勢，然而，在現今資訊爆炸的時代，我們不可能去閱讀所有的訊息，即使只是保持從幾個資訊來源得到最新的資訊也是非常耗費時間的，並且有可能會延遲與不斷變化的科技和商業環境的互動。
本論文提出一個科技觀測服務來監測商業生態系統的演化，藉由自動的從不同的資訊來源中擷取關係來建立商業生態系統；為了建立由公司、產品及科技所組成的商業生態系統，本研究加強關係擷取方法從多個不同的實體名稱(Name entity)類別來抓取之間互動的關係。
透過本系統所提供的服務，期望藉由商業生態系統及由文字探勘所萃取出的其他資訊，可以幫助公司預測未來的科技趨勢以及市場狀況，而為了減少公司監測的成本，本系統可以自動抓取公司有興趣的資訊和即時且有效率的抓取跟商業生態系統演化相關的訊息。

Chapter 1 Introduction 1
1.1 Research Background 1
1.2 Research Motivation 2
1.3 Research Objectives 2
Chapter 2 Literature Review 4
2.1 Technology Mining 4
2.2 Information Extraction (IE) 6
2.2.1 Name Entity Recognition (NER) 7
2.2.2 Relation Extraction 10
2.3 Normalized Google Distance (NGD) 12
Chapter 3 Research Framework 14
3.1 Technology Monitoring Platform 15
3.2 Information Source Service 16
3.3 Text Mining Service 16
3.3.1 Quotation Extraction 17
3.3.2 Statement Extraction 17
3.3.3 Relation Extraction 17
3.4 Customized Visualization Service 27
Chapter 4 System Implementation 28
4.1 Data Sources 28
4.2 Visualization 28
Chapter 5 Experimental Design and Results 32
5.1 Data Description 32
5.2 Evaluation 33
5.3 Experimental Design 33
5.4 Experimental Results and Discussion 35
Chapter 6 Conclusion and Future Work 39
References 40
Appendix A. The 24 words used in the evaluation 42
Appendix B. The distance between candidate relations and restriction list smaller than 0.4 43

Adomavicius, G., Bockstedt, J.C., Gupta, A., & Kauffman, R. J. (2007). Technology roles and paths of influence in an ecosystem model of technology evolution. Information Technology and Management, 8(2), 185-202.
Appelt, D. E., & Israel, D. (1999). Introduction to Information Extraction Technology. A tutorial prepared for IJCAI-99," Artificial Intelligence Center, SRI International.
Cilibrasi, R. L., & Vitanyi, P.M.B. (2007). The Google Similarity Distance. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 19(3), 370-383.
Cunningham, H. (2005) "Information Extraction, Automatic," Encyclopedia of Language and Linguistics. Elsevier..
Fundel, K., Kuffner, R., & Zimmer, R. (2007). RelEx—Relation extraction using dependency parse trees. Bioinformatics , 23(3), 365-371.
Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004). Design Science in Information Systems Research. MIS Quarterly, 28(1), 75-105.
Kongthon, A (2004). A Text Mining Framework for Discovering Technological Intelligence to Support Science and Technology Management (Unpublished doctoral dissertation). Georgia Institute of Technology.
Kushmerick, N (1997). Wrapper induction for information extraction (Unpublished doctoral dissertation). University of Washington.
Losiewicz, P., Oard, D., & Kostoff, R (2000). "Text Data Mining to Support Science and technology management," Journal of Intelligent Information Systems 15(2), 99-119.
Mansouri, A., Affendey, L. S., & Mamat, A. (2008). Named Entity Recognition Approaches. International Journal of Computer Science and Network Security, 8 (2), 339-344.
Marneffe, M.-C., MacCartney, B., & Manning, C. D. (2006). Generating Typed Dependency Parses from Phrase Structure Parses.
Porter, A. L., & Cunningham, S. W. (2005). Tech Mining: Exploiting New Technologies for Competitive Advantage. United States of America: John Wiley & Sons, Inc.
Thomson R. (2009). Home | OpenCalais. Retrieved 12 24, 2009, from OpenCalais: http://www.opencalais.com/
Varelas, G., Voutsakis, E., Raftopoulou, P., Petrakis, E.G.M., & Milios, E.E. (2005). Semantic similarity methods in wordNet and their application to information retrieval on the web. In Proceedings of the seventh ACM international workshop on Web information and data management (pp. 10-16).
Yuan, J., and Zhu, D. (2004). A Study on Technology Monitoring Based on Text Mining to Support Science and Technology Management. In Proceedings of World Engineers' Convention, (pp. 47-51).