隨著知識經濟的蓬勃發展，企業競爭優勢之提昇有賴知識的創新與應用。因此，有效地實施知識管理已成為企業成功的關鍵因素之一。由於知識本體(Domain Ontology)能適當地描述特定領域之知識組成與知識間之關係，知識管理的成效則繫於知識本體(Domain Ontology)的之建置、運用與維護。
本研究之主要目的在發展一個人化知識搜尋與推薦之領域知識本體調適機制。依據使用者之需求與使用行為(Usage History Log)，本機制可調適出的個人化領域知識本體，以更正確地反應出合適之領域知識進而提昇領域知識本體的使用價值。為達上述目的，本研究主要研究項目包括: (i) 知識本體之調適模式設計，(ii) 知識本體之調適方法發展與(iii) 知識本體之調適機制實作。知識本體之調適模式設計包括知識本體之調適因子分析模式設計、使用者之使用情境分析模式建立與個人化調適模式建立。知識本體之調適方法發展則包括知識本體之調適程序建構、調適因子分析方法發展、使用者之偏好分析方法發展、使用需求為基概念分群方法發展與個人化調適方法發展。

With the rapid development of knowledge economy, the competitiveness of enterprises relies heavily on innovation and application of knowledge. Therefore, effective management of knowledge has become one of the critical factors of enterprises, which is further dependent on the successful building, application and maintenance of ontology due to its feasibility for domain knowledge representation.
The objective of this thesis is to develop an ontology adaptation mechanism for personalized knowledge search and recommendation. According to user requirements and usage history log, a personalized ontology is developed to more precisely reflect the domain knowledge in need and thus increase the value of domain ontology. The above objective can be achieved through: (i) domain ontology adaptation model design, (ii) domain ontology adaptation method development, and (iii) system implementation with a case study. Domain ontology adaptation model design includes adaptation factor analysis, user scenario analysis and adaptation model development. And, domain ontology adaptation method development includes the developments of adaptation procedure, user preference analysis, and requirement-based concept clustering.
The results of this research can enhance the accuracy of the domain ontology and personalized knowledge search and recommendation, and thus enhance the competitiveness of enterprises.

[1] Agresti, A., Categorical Data Analysis. Wiley, 1990.
[2] Amemiya, T., Advanced Econometrics, Harvard University Press, 1985.
[3] Baneyx, A., Charlet, J. and Jaulent, M.-C., “Building an ontology of pulmonary diseases with natural language processing tools using textual corpora”, International Journal of Medical Informatics, Vol. 76, No. 2-3, pp. 208-215, 2007.
[4] Borst, P., Akkermans, H., and Top, J., “Engineering ontologies”, International Journal of Human-Computer Studies, Vol. 46, No. 2-3, pp. 365-406, 1997.
[5] Chen, Y. L., Kuo, M. H., Wu, S. Y. and Tang, K., “Discovering recency, frequency, and monetary (RFM) sequential patterns from customers’ purchasing data”, Electronic Commerce Research and Applications, Vol. 8, No. 5, pp. 241-251, 2009.
[6] Cheung, K., Hunter, J. and Drennan, J., “MatSeek:An Ontology-Based Federated Search Interface for Materials Scientists”, Semantic Scientific Knowledge Integration, Vol. 24, No. 1, pp. 47-56, 2009.
[7] Chen, R. C., Liang, J. Y. and Pan, R. H., “Using recursive ART network to construction domain ontology based on term frequency and inverse document frequency”, Expert Systems with Applications, Vol. 34, No. 1, pp. 488–501, 2008.
[8] Erozel, G.,Cicekli, N. K. and Cicekli, I., “Natural language querying for video databases”, Information Sciences, Vol. 178, No. 12, pp. 2534–2552, 2008.
[9] Figge, S., “Situation-dependent services? A challenge for mobile network Operators”, Journal of Business Research, Vol. 57, No. 12, pp. 1416–1422, 2004.
[10] Forte, M., Souza, W. L., Prado, A. F., “Using ontologies and Web services for content adaptation in Ubiquitous Computing”, The Journal of Systems and Software, Vol. 81, No. 3, pp. 368–381, 2008.
[11] Freeman, L., “The Development of Social Network Analysis”, Vancouver: Empirical Press, 2006.
[12] Gruber, T. R., “A translation approach to portable ontology specifications”, Knowledge Acquisition, Vol. 5, No. 2, pp. 199-220, 1993.
[13] Hong, J., Suh , E. H., Kim, J. and Kim, S. Y., “Context-aware system for proactive personalized service based on context history”, Expert Systems with Applications, Vol. 36, No. 4, pp. 7448–7457, 2009.
[14] Hosmer, D. W. and S. Lemeshow, Applied logistic regression, Wiley, 2000.
[15] Hsu, C. C., Chen, C. L. and Su Y. W., “Hierarchical clustering of mixed data based on distance hierarchy”, Information Sciences, Vol. 177, No. 20, pp. 4474-4492, 2007.
[16] Khasawneh, N. and Chan, C. C., “Active User-Based and Ontology-Based Web Log Data Preprocessing for Web Usage Mining”, Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence, pp. 325-328, 2006.
[17] Komlodi, A., Marchionini, G. and Soergel, D., “Search history support for finding and using information: User interface design recommendations from a user study”, Information Processing and Management, Vol. 43, No. 1, pp. 10–29, 2007.
[18] Kwon, O., “A social network approach to resolving group-level conflict in context-aware services”, Expert Systems with Applications, Vol. 36, No. 5, pp. 8967–8974, 2009.
[19] Lai, L. F., “A knowledge engineering approach to knowledge management”, Information Sciences, Vol. 177, No. 19, pp. 4072-4094, 2007.
[20] Lee,C. S., Liao, C. H. and Kuo, Y. H., “A Semantic-based Concept Clustering Mechanism for Chinese News Ontology Construction,” International Computer Symposium, Taiwan, 2002.
[21] Lee, W. P., “Deploying personalized mobile services in an agent-based Environment”, Expert Systems with Applications, Vol. 32, No. 4, pp. 1194–1207, 2007.
[22] Lee, I., Kim, J. and Kim, J., “Use contexts for the mobile Internet: A longitudinal study monitoring actual use of mobile Internet services”, International Journal of Human–Computer Interaction, Vol. 18, No. 3, pp. 269–292, 2005.
[23] Li, H. C. and Ko, W. M., “Automated Food Ontology Construction Mechanism for Diabetes Diet Care”, Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, Hong Kong, 2007.
[24] Lia, Y. F. and Zhong, N., “Web mining model and its applications for information gathering”, Knowledge-Based Systems, Vol. 17, No. 5, pp. 207–217, 2004.
[25] Liao, C. W., Perng, Y. H. and Chiang, T. L., “Discovery of unapparent association rules based on extracted probability”, Decision Support Systems, Vol. 47, No. 4, pp. 354–363, 2009.
[26] Liu, M., Shen, W., Hao, Q. and Yan J., “An weighted ontology-based semantic similarity algorithm for web service”, Expert Systems with Applications, Vol.36, No. 10, pp. 12480-12490, 2009.
[27] Noy, N. F. and McGuinness, D. L., “Ontology Development 101: A Guide to Creating Your First Ontology”, Technical report, 2001.
[28] Plessers, P., De Troyer, O. and Casteleyn, S., “Understanding ontology evolution: A change detection approach”, Web Semantics: Science, Services and Agents on the World Wide Web, Vol. 5, No. 1, pp. 39-49, 2007.
[29] Plessers, P. and De Troyer, O., “Resolving Inconsistencies in Evolving Ontologies”, Lecture Notes in Computer Science, Vol. 4011, pp. 200-214, 2006.
[30] Pinto, H. S. and Martins, J. P., “Evolving ontologies in distributed and dynamic settings”, In Proceedings of the Eighth International Conference on Principles of Knowledge Representation and Reasoning (KR2002), Toulouse, France, 2002.
[31] Quan, T. T., Hui, S. C., Fong, A. C. M., and Cao, T. H., “Automatic Fuzzy Ontology Generation for Semantic Web”, IEEE Transactions on Knowledge and Data Engineering, Vol. 18, No. 6, pp. 842-856, 2006.
[32] Rostami, H., Habibi, J. and Livani, E., “Semantic routing of search queries in P2P networks”, Journal of Parallel and Distributed Computing, Vol. 68, No. 12, pp. 1590-1602, 2008.
[33] Shamsfard, M. and Barforoush, A. A., “Learning ontologies from natural language texts”, International Journal of Human-Computer Studies, Vol. 60, pp. 17-63, 2004.
[34] Stanojevic, M. and Vraneš, S., “Knowledge representation with SOUL”, Expert Systems With Applications, Vol. 33, No. 1, pp. 122-134, 2007.
[35] Sufyan Beg, M.M., “A subjective measure of web search quality”, Information Sciences, Vol. 169, No. 3, pp.365–381, 2005.
[36] Swartout, B., Patil, R., Knight, K. and Russ, T., “Toward Distributed Use of Large-Scale Ontologies”, Proceedins of 10th Knowledge Acqusition for Knowledge-based System Workshop, 1996.
[37] Tan, P. N., Steinbeach, M. and Kumar, V., “Introduction to Data Mining”, Addison Wesley, pp.158-162, 2006.
[38] Tan, P. N., Steinbeach, M. and Kumar, V., “Introduction to Data Mining”, Addison Wesley, pp.328-330, 2006.
[39] Tan, P. N., Steinbeach, M. and Kumar, V., “Introduction to Data Mining”, Addison Wesley, pp.515-523, 2006.
[40] Tian, X., Du, X. Y., Hu, H. and Li, H. H., “Modeling individual cognitive structure in contextual information retrieval”, Computers and Mathematics with Applications, Vol. 57, No. 6, pp. 1048-1056, 2009.
[41] Tury, M. and Bielikov´a , M., “An approach to detection ontology changes”, ACM International Conference On Web Engineering, Vol. 155, 2006.
[42] Uschold, M. and Gruninger, M., “Ontologies: Principles Methods and Applications”, Knowledge Engineering Review, Vol. 11, No. 2, pp.93-155, 1996.
[43] Weng, S. S. and Chang, H. L., “Using ontology network analysis for research document recommendation”, Expert Systems with Applications, Vol. 34, No. 3, pp. 1857-1869, 2008.
[44] Weng, S. S., Tsai, H. J., Liu, S. C., and Hsu, C. H., “Ontology Construction for information classification”, Expert System with Applications, Vol. 31, No. 1, pp. 1-12, 2006.
[45] “http://weka.wiki.sourceforge.net/ARFF”, Weka 3: Data Mining Software in Java.
[46] “http://www.uml.org/”, Unified Modeling Language™.