由於市場環境快速變遷，企業或個人面臨的困難及挑戰倍增，遭遇的問題更是複雜且多元。知識需求若能迅速從知識市場獲取知識，以解決問題。但知識需求者通常無法從單一知識內容獲取完整所需之知識。因此知識商務將為企業帶來知識資產管理的創新思維及獲利模式。知識商品眾多，以人工方式進行知識組合、尋找和比對知識相當耗時費力，又知識商品具私有性。因此，如何以自動化的方法組合適當之知識商品，以滿足知識需求者之客製化知識之需求，為一個重要的議題。
為了滿足前述需求，本研究發展一「知識商務之具互補性知識商品推薦研究」，依據知識需求者所輸入之知識需求，轉換為知識需求本體，並與知識商品本體資料庫做搜尋比對，找出與知識需求者相關之知識商品，最後依據知識商品組合指標，建立一知識商品組合方法，並採用基因演算法以求取最適解。
藉由本研究發展之機制，以解決尋找和比對知識所耗費時間和成本，並可推薦最適知識商品組合給知識需求者作為選購之參考，以促進知識商品的交易。

In the rapidly changing business environment, enterprises or individuals encounter more difficulties and challenges, and their encountered problems are also more complex and diverse. To sustain their competitive advantage, knowledge requirements need to be able to acquire knowledge quickly to solve their problems. Knowledge commerce (k-commerce) brings innovative thinking and profit models of knowledge assets management for enterprises. However, knowledge product is private, and search a desired knowledge product by hand form a lot of knowledge products is very time-consuming. Besides, a single knowledge product usually cannot satisfy the complex problem. Therefore, how to recommend the appropriate combination of knowledge products for satisfying customized knowledge requirement is an important issue.
To overcome the above problem, this study develops a complementary knowledge product recommendation mechanism according to knowledge requesters’ requirements. At first, this study designs a structured representation model of knowledge requirement. Subsequently, this study proposes a similarity approach to match related knowledge products based on the knowledge requester’s requirement from the knowledge product ontology base. Finally, this study proposes a knowledge product combination approach using genetic algorithms to recommend the optimal combination of knowledge products according to knowledge product combination indicators.
The mechanism effectively provides the desired knowledge for knowledge requester, thus facilitates successful knowledge products transactions.

英文文獻
1.Alavi, M., & Leidner, D. E. (2001) Review: Knowledge Management and Knowledge Management System: Conceptual Foundations and Research Issues.MIS Quarterly, 25(1), pp.107-136.
2.Aydogan, N., & Lyon, T. (2004) Spatial proximity and complementarities in the trading of tacit knowledge. International Journal of Industrial Organization, 22(8-9), pp. 1115-1135.
3.Blick, R. J., Revel, A. T., & Hansen, E. J. (2003) FindGDPs: identification of primers for labeling microbial transcriptomes for DNA microarray analysis. Bioinformatics, 19, pp.1718-1719.
4.Chen, K. J., & Liu, S. H. (1992) Word Identification for Mandarin Chinese Sentences. Proceeding of COLING-92, 14th Int. Conf. On Computational Linguistics, Nantes, France, pp. 101-107.
5.Chien, L. F. (1997) PAT-Tree-Based Keyword Extraction for Chinese Information retrieval. Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval, ACM New York, NY, USA, pp. 50-58.
6.Cho, H. Y., & Kim, J. K. (2004) Application of web usage mining and product taxonomy to collaborative recommendations in e-commerce. Expert Systems with Applications, 26(2), pp.233-246.
7.Daconta, M. C., Obrst, L. J., & Smith, K. T. (2003) The Semantic Web: A Guide to the Future of XML, Web Services, and Knowledge Management. Wiley Publishing, Inc., United States of America.
8.Davenport, T. H., & Prusak, L. (1998) Working Knowledge： how organization manage what they know. Harvard Business School Press, Boston Mass.
9.Dorigo, M. (1992). Optimization, learning and natural algorithms. PhD thesis, Politecnico di Milano, Italy.
10.Eberhart, R. C., & Kennedy, J. (1995) A new optimizer using particle swarm theory. Proceedings of the Sixth International Symposium on Micromachine and Human Science, Nagoya, Japan, pp. 39-43.
11.Fan, C. K., & Tsai, W. H. (1988) Automatic Word Identification in Chinese Sentences by the Relaxation Technique. Computer Processing of Chinese and Oriental Languages, 2(4), pp. 33-56.
12.Formica, A. (2008) Concept similarity in Formal Concept Analysis: An information content approach. Knowledge-Based System, 21(1), pp.80-87.
13.Gauch, S., & Chong, M. K. (1995) Automatic Word Similarity Detection for TREC 4 Query Expansion. Proc. of TREC-4: The 4th Annual Text REtrieval Conf., Nov., Gaithersburg, MD, pp. 527-536.
14.Goldberg, D., Nichols, D., Oki, B. M., & Terry, D. (1992) Using Collaborative Filtering to Weave an Information Tapestry. Communication of the ACM, December, 35(12), pp.61-70.
15.Gronau, N., Fro¨ming J., Schmid, S., Ru¨ssbu¨ldt U. (2007) Approach for requirement oriented team building in industrial processes. Computers in Industry, 58(2), pp.179–187.
16.Gruber, T. R. (1993) A Translation Approach to Portable Ontologies. Knowledge
Acquisition, 5(2), pp. 199-220.
17.Guarino, N. (1998) Formal ontology and information systems. Proceedings of FOIS’98, Trento, Italy, 6-8 June, Amsterdam, IOS Press, pp. 3-15.
18.Guha, S., Rastogi, R., & Shim, K. (1998) Cure: An efficient clustering algorithm for large databases. ACM SIGMOD International Conference on Management of Data, Seattle, Washington, USA, pp.73-84.
19.Guo, Z., Wang, D., & Wu, J. (2006) Application of Stochastic Approximation to Digital Knowledge Products Pricing in Electronic Commerce. Proceedings of the 6th World Congress on Intelligent Control and Automation, June 21 - 23, Dalian, China, pp. 21 – 23.
20.Hedlund, G. (1994) A model of knowledge management and theN-form corporation. Strategic Management Journal, 15, pp. 73-90.
21.Holland, J. H. (1975) Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT Press, 1st MIT Press edition, 1992. University of Michigan Press, 1st edition.
22.Huang, C. C., Liang, W. Y., Lai, Y. H., & Lin, Y. C. (2010) The agent-based negotiation process for B2C e-commerce. Expert Systems with Applications. 37(1), pp. 348-359.
23.Hwang, S. Y., Wei C. P., & Liao Y. F. (2010) Coauthorship networks and academic literature recommendation. Electronic Commerce Research and Applications, In Press, Corrected Proof.
24.Hwang, G. J., Yin, P. Y., Hwang, C. W., & Tsai, C. C. (2008) An Enhanced Genetic Approach to Composing Cooperative Learning Groups for Multiple Grouping Criteria. Educational Technology and Society, 11(1), pp.148-167.
25.Kafentzis, K., Georgolios, P., Bouras A., & Mentzas, G. (2006) An Ontology-based Architecture for Knowledge Commerce. Proceedings of the 39th Hawaii International Conference on System Sciences, 7, pp.160a - 160a.
26.Kirkpatrick, S. et.al. (1983) Optimization by simulated annealing. Science, 220 pp. 671-80.
27.Kong, H., Hwang, M., & Kim, P. (2005) A new methodology for merging the heterogeneous domain ontologies based on the wordnet. Proceedings of the International Conference on Next Generation Web Services Practices, IEEE Computer Society, Washington, DC, USA, pp. 235.
28.Konstan, J. A., Miller, B. N., & Maltz, D. (1997) GroupLens: Applying collaborative filtering to Usenet news. Communications of the ACM, 40(3), pp.77-87.
29.Lang, K. (1995) NewsWeeder: Learning to filter netnews. Proceedings of the 12th International Conference on Machine Learning, San Francisco, CA. Morgan Kaufman, pp.331-339.
30.Lee, C. S., Chen, C. P., Chen, H. J., & Kuo, Y. H. (2002) A fuzzy classification agent for personal e-news service. International Journal of Fuzzy Systems, 4(4), pp. 849-856.
31.Lin, D. (1998) An Information-Theoretic Definition of Similarity. Proceedings of the 15th International Conference on Machine Learning (ICML-98), San Francisco, CA, USA: Madison, WI, USA, Morgan Kaufmann, pp. 296–304.
32.LI, X., Szpakowicz S., & Matwin, S. (1995) A WordNet-based algorithm for word sense disambiguation. Proc. of the Twelth International Joint Conference on Artificial Intelligence (IJCAI), European, pp. 1368-1374.
33.Li, Y., & Zhong, N. (2004) Web mining model and its applications for information gathering.Knowledge-base systems, 17, pp.207-217.
34.Li, Z., & Xing, L. (1998) Search the Chinese Web -Design and the Operation of Net-Compass. Proceedings of the First Asia Digital Library Workshop, pp. 42-46.
35.Marianne, L. (1987) The knowledge acquisition grid: a method for training knowledge engineers. International Journal of Man-Machine Studies, 26, pp. 245-255.
36.McGuinness, D. L., & Harmelen, F. v. (2003) OWL Web Ontology Language Overview. http://www.w3.org/TR/2003/PR-owl-features-20031215/.
37.Minsky, M. (1975) A Framework for Representing Knowledge. The Psychology of Computer Vision, P. H. Winston, McGraw-Hill.
38.Mitchell, M. (1996) An Introduction to Genetic Algorithms. MIT Press, Cambridge, MA.
39.Negnevitsky, M. (2002) Artificial Intelligence-A Guide to Intelligent Systems, Addison-Wesley, England.
40.Nie, J. Y., Hannan M. L., & Jin W. (1995) Combining Dictionary, Rules and Statistical Information in Segmentation of Chinese. Computer Processing of Chinese and Oriental Languages, 9, pp. 125-143.
41.Nonaka, I., & Takeuchi, H. (1995) The knowledge creating company: How Japanese Companies Create the Dynamic of Innovation. New York: Oxford University Press.
42.Noy, N. F., & Musen, M. A. (2003) The PROMPT suite: interactive tools for ontology merging and mapping. International Journal of Human-Computer Studies, 59, pp. 983-1024.
43.Payne, P. R. O. M., Eneida, A. Johnson, Stephen B. & Starren, Justin B. (2007) Conceptual knowledge acquisition in biomedicine: A methodological review. Journal of Biomedical Informatics, 40(5), pp. 582-602.
44.Quinn, J., Anderson, P., & Finkelstein, S. (1996) Managing professional intellect: making the most of the best. Harvard Business Review, 74, pp. 71-80.
45.Rashid, A. M., Albert, I., Cosley, D., Lam, S. K., McNee, S. M., Konstan, J. A.,& Riedl, J. (2002) Getting to know you: Learning new user preferences in recommender systems. In Proceedings of the 2002 International Conference on Intelligent User Interfaces, San Francisco, CA, pp. 127-134.
46.Reigeluth, Charles M., & Frick, T. W. (1999) Formative research: A methodology for creating and improving design theories. Instructional design theories and models: A new paradigm of instructional theory, Hillsdale, pp. 5-29.
47.Robert, J. Blick, Andrew T. Revel & Eric J. Hansen, (2003) FindGDPs: dentification of primers for labeling microbial transcriptomes for DNA microarray analysis. Bioinformatics, 19, pp: 1718-1719.
48.Russet, S., & Norvig, P. (2002) Artificial Intelligence: A Modern Approach. Pearson Education, India.
49.Salton, G. (1975) A Theory of Indexing, Regional Conference Series in Application Mathematics. Society for Industrial and Applied Mathematics, pp. 318.
50.Sanjiv, K. B., & Jitender, S. D. (1993) Cluster Characterization in Information Retrieval. In Proceedings of the 8th ACM/SIGAPP symposium on Applied computing: states of the art and practice, ACM Press, Indianapolis, pp.721-728.
51.Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2000) Analysis of recommendation algorithms for e-commerce. Proceedings of the 2nd ACM conference on E-Commerce, New York, pp.158-167.
52.Skyrme, D. J. (2001) Capitalizing on Knowledge: From E-Business to K-Business. Butterworth-Heinemann, United Kingdom.
53.Smith, B., & Welty, C. (2001) Ontology: Toward a New Synthesis. Proceedings of the international conference on Formal Ontology in Information Systems, Ogunquit, Maine, USA, pp.3-9.
54.Sporat, R., Shih, C., Gale, W., & Chang, N. (1996) A Stochastic Finite-State Word-Segmentation Algorithm for Chinese. Computational Linguistics, 22(3), pp. 377-404.
55.Sporat, R., & Shih, C. (1990) A Statistical Method for Finding Word Boundaries in Chinese Text. Computer Processing of Chinese and Oriental Languages, 4(4), pp. 336-351.
56.Staab, S., & Studer, R. (2001) Knowledge Processes and Ontologies. IEEE Intelligent Systems, 16(1), pp.26-34.
57.Storey, V. C. (2005) Comparing Relationships in Conceptual Modeling: Mapping to Semantic Classifications. IEEE Transactions on Knowledge and Data Engineering, 17(11), November.
58.Tversky, A. (1977) Features of Similarity. Psychological Review, 84(4), pp. 327-352.
59.Uschold, M., & Gruninger, M. (1996) Ontologies: Principles, Methods and Applications. The Knowledge Engineering Review, 11(2), pp. 93-136.
60.Wiig, K. M. (1993) Knowledge Management Foundations: Thinking about Thinking–How People and Organizations Create, Represent, and Use Knowledge., TX: Schema Press, Arlington.
61.Yang, C., Yen, J., & Yung, S. (1998) Chinese Indexing Using Mutual Information. Proceedings of the First Asia Digital Library Workshop, pp. 57-64.
62.Yeh, C. L., & Lee, H. J. (1991) Rule-Based Word Identification for Mandarin Chinese Sentences-A Unification Approach. Computer Processing of Chinese and Oriental Languages, 5(2), pp. 97-118.
中文文獻
1.中央研究院中文斷詞系統，URL：http://ckipsvr.iis.sinica.edu.tw/。
2.王允成(2002)。「以M42底火膛壓曲線作灰預測循跡之研究」，國防大學中正理工學院兵器系統工程研究所碩士論文。
3.王良志、貝子勝、黎偉權和黃麗卿(1991)，「以剖析為導向的中文斷詞法」，電子發展月刊，163 期， 第40-45頁。
4.王斌(1999)。「漢英雙語語料庫自動對齊研究」，中國科學院計算技術研究所博士學位論文。
5.江振宇(2002)。「中文斷詞器之改進」，國立交通大學電信工程學系碩士論文。
6.余青山(2003)。「知識價值簡論」，中國經濟問題，第一期，第65-67頁。
7.李志豪(2005)。「以基因規劃法為基礎的中文斷詞模型」，玄奘大學資訊科學系碩士論文。
8.李涓子(1999)。「漢語詞義排歧方法研究」，清華大學計算機科學與技術博士論文。
9.林奕銘(2007)。「知識工作者與組織知識創造：社會困境的問題與解決」，國立虎尾科技大學學報，第二十六卷，第三期，第75-90頁。
10.界屋太一(1987)。「智價革命」。台北市：遠流出版社。
11.范長康和蔡文祥(1987)。「以鬆弛法作中文斷詞」，全國計算機會議論文集，第423-431頁。
12.孫光天、陳岳宏、賴膺守、謝凱隆和陳新豐(1999)。「使用貪婪演算法作為一有效益之選題策略」，全國計算機會議(NCS 99’)，台北：淡江大學。
13.孫樹永(2008)。「知識管理運用於銀行業不動產鑑估業務之探討-以本國某大型銀行為例」，玄奘大學公共事務管理學系碩士論文。
14.徐靖雯(2006)。「Bray-Curtis 指標與其他相似度指標之模擬探討」，國立清華大學統計學研究所碩士論文。
15.曹哲嘉(2004)。「跨越邊界之無線通訊應用創新：以本體論為基礎的知識組合分析與個案研究」，國立清華大學工業工程與工程管理學系碩士論文。
16.陳克建、陳正佳和林隆基(1986)。「中文語句分析的研究-斷詞與構詞」，中央研究院資訊所技術報告，TR86-004。
17.陳育銘(2006)。「結合5W1H與本體論進行網路資料探勘技術之研究」，南華大學資訊管理學系碩士論文。
18.陳怡燕(2006)。「以本體論為基之知識整合機制研發」，國立成功大學製造資訊與系統所碩士論文。
19.傅振焜譯(1994)。Peter Drucker著，後資本主義社會，台北：時報。
20.黃雲龍和張佑任(2002)。「中文全文資訊檢索之效能評量初探」，南華大學資訊管理學刊，第2期。
21.趙伯偉(2007)。「本體論為基之產品生命週期知識整合機制研發」，國立成功大學製造資訊與系統所碩士論文。
22.劉均勻(1999)。「試論知識商品與知識貿易」，湖南大學學報，第十三期，第二卷，第22-27頁。
23.蔡文鈞、賴鈺晶和吳思華(2004)。「知識型商品擴散模式之理論性探討」。科技管理學刊，第九期，第三卷，第117-152頁。
24.蔡明璋(2007)。「傳統產業專業知識延續管理之研究—以女性飾品服裝A公司研發設計人員為訪談案例」，中原大學企業管理學系碩士論文。
25.簡伶容(2008)。「本體論為基之意圖感知問題導向學習機制研發」，國立成功大學製造資訊與系統所碩士論文。
26.關銘(2004)。「以OWL DL及SWRL為基礎建置推論雛形系統-以大學排課問題為例」，中原大學資訊管理研究所碩士論文。