知識為現今經濟體系中最重要的資源，因此企業須有效地執行知識管理相關策略，在適當的時機提供正確的知識給對的使用者，以產生最高效益。而正確地表達知識為知識管理之基礎與成敗關鍵。知識本體結構化的知識表達模式有助於不同概念或語意間的轉換、交換與再利用，進而協助使用者更加流暢地運用知識，是目前最廣泛被接受的知識表達工具，然而快速成長的知識將導致領域知識本體完整性不足，並降低其使用價值。
本研究之目的為發展一強化領域知識本體之需求導向知識獲取方法，利用使用者之知識需求獲取領域知識本體所缺乏之知識概念，並與領域知識本體整合，以加強領域知識本體之完整性，進而提升其使用價值。為達上述研究目的，本研究主要研究項目包括(1) 強化領域知識本體之需求導向知識獲取流程設計，(2)需求前處理方法發展，(3)知識擷取與搜尋方法發展，(4)知識本體建構方法發展，(5)知識本體整合方法發展(6)強化領域知識本體之需求導向知識獲取機制實作。

Knowledge has been the most important resource in the contemporary economic system. Enterprises need to take effective knowledge- management strategies to provide right knowledge to appropriate knowledge workers at a suitable time in order gain highest benefit. However, accurate knowledge representation is a fundamental and critical point for knowledge management among enterprises. Ontologies are the most popular and acceptable technology to represent domain knowledge due to its structurized representing fashion which performs well in semantic transition, transaction and reuse for knowledge concepts to the end of applying knowledge more smoothly by knowledge user. But the rapidly growth of knowledge with more and more interdisciplinary knowledge workers may relatively decrease the integrity of domain ontologies which reduces its value somehow.
This study proposed a Demand-Driven Knowledge Acquisition Method for enhancing the integrity of domain ontologies. This method acquires and integrates knowledge concepts which the original domain ontology lacked according to users’ knowledge demand in order to increases the value of domain ontologies. According to above mentioned purpose, the study first design a process model of “Demand-Driven Knowledge Acquisition for Enhancing Domain Ontology” and then develops following methods according to such model: (1) Demand Preprocessing, (2) Knowledge Retrieval and Searching, (3) Ontology Construction, (4) Ontology Integration. Finally, implement such model as a mechanism.

[1] Aerts, S. (2011). Dimensional Reduction in Vector Space Methods for Natural Language Processing: Products and Projections. International Journal of Theoretical Physics, 50(12), 3646–3653.
[2] Agirre, E., Rigau, G. (1996). Word sense disambiguation using Conceptual Density. Proceedings of the 16th conference on Computational linguistics - Volume 1 (pp. 16–22). Copenhagen, Denmark: Association for Computational Linguistics.
[3] Agirre, E., Martínez, D., de Lacalle, O., Soroa, A. (2006). Two graph-based algorithms for state-of-the-art WSD. Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing (pp. 585–593). Association for Computational Linguistics.
[4] Ahmed, S., Kim, S., Wallace, K. M. (2007). A Methodology for Creating Ontologies for Engineering Design. Journal of Computing and Information Science in Engineering, 7(2), 132–140.
[5] Alani, H., Kim, S., Millard, D. E., Weal, M. J., Hall, W., Lewis, P. H., Shadbolt, N. R. (2003). Automatic Ontology-Based Knowledge Extraction from Web Documents. IEEE Intelligent Systems, 18(1), 14–21.
[6] Baeza-Yates, R., Ribeiro-Neto, B. (1999). Modern Information Retrieval. Addison Wesley.
[7] Banerjee, S., Pedersen, T. (2003). Extended gloss overlaps as a measure of semantic relatedness. Proceedings of the 18th international joint conference on Artificial intelligence (pp. 805–810). Acapulco, Mexico: Morgan Kaufmann Publishers Inc.
[8] Borst, P., Akkermans, H., Top, J. (1997). Engineering ontologies. International Journal of Human-Computer Studies, 46(2–3), 365–406.
[9] Bouaud, J., Bachimont, B., Charlet, J., Zweigenbaum, P. (1994). Acquisition And Structuring Of An Ontology Within Conceptual Graphs. University of Maryland, College Park, MD (pp. 1–25).
[10] Burchfield, R. (1985). Frequency Analysis of English Usage: Lexicon and Grammar. By W. Nelson Francis and Henry Kucera with the assistance of Andrew W. Mackie. Boston: Houghton Mifflin. 1982. x + 561. Journal of English Linguistics, 18(1), 64–70.
[11] Buchanan, B.G., Barstow, D., Bechtal, R., Bennett, J., Clancy, W., Kulikowski, C., Mitchell, T., Waterman, D.A., 1983. Constructing an expert system. In: Hayses -Roth, F., Waterman, D.A., Lenat, D.B. (Eds.), Building expert system. Addison-Wesley, Reading, MA, pp. 127–167.
[12] Chen, R.C., Bau, C. T., Yeh, C. J. (2011). Merging domain ontologies based on the WordNet system and Fuzzy Formal Concept Analysis techniques. Applied Soft Computing, 11(2), 1908–1923.
[13] Chen, Y., Zhang, Y. Q. (2009). Extracting Concepts’ Relations and Users’ Preferences for Personalizing Query Disambiguation.
[14] Chen, Y. J., Chen, Y. M., Chu, H. C. (2009). Development of a mechanism for ontology-based product lifecycle knowledge integration. Expert Systems with Applications, 36(2, Part 2), 2759–2779.
[15] Chen, Y. L., Kuo, M.-H., Wu, S. Y., Tang, K. (2009). Discovering recency, frequency, and monetary (RFM) sequential patterns from customers’ purchasing data. Electronic Commerce Research and Applications, 8(5), 241–251.
[16] De Maio, C., Fenza, G., Gaeta, M., Loia, V., Orciuoli, F., Senatore, S. (2012). RSS-based e-learning recommendations exploiting fuzzy FCA for Knowledge Modeling. Applied Soft Computing, 12(1), 113–124.
[17] De Maio, Carmen, Fenza, G., Loia, V., Senatore, S. (2012). Hierarchical web resources retrieval by exploiting Fuzzy Formal Concept Analysis. Information Processing & Management, 48(3), 399–418.
[18] Duong, T. (2009). Complexity analysis of ontology integration methodologies: A comparative study. Journal of Universal Computer Science, 15(4), 877.
[19] Duong, T. H., Nguyen, N. T., Jo, G. S. (2009). A HYBRID METHOD FOR INTEGRATING MULTIPLE ONTOLOGIES. Cybernetics and Systems, 40(2), 123–145.
[20] Fernandez-Amoros, D., Heradio, R. (2011). Understanding the role of conceptual relations in Word Sense Disambiguation. Expert Syst. Appl., 38(8), 9506–9516.
[21] Garcés, P. J., Olivas, J. A., Romero, F. P. (2006). Concept-matching IR systems versus word-matching information retrieval systems: Considering fuzzy interrelations for indexing Web pages. Journal of the American Society for Information Science and Technology, 57(4), 564–576.
[22] Gilson, O., Silva, N., Grant, P. W., Chen, M. (2008). From Web Data to Visualization via Ontology Mapping. Computer Graphics Forum, 27(3), 959–966.
[23] Gruber, T. R. (1993). A translation approach to portable ontology specifications. Knowl. Acquis., 5(2), 199–220.
[24] Guo, P., Wang, X. R., Kang, Y. R. (2006). Frequent mining of subgraph structures. Journal of Experimental & Theoretical Artificial Intelligence, 18(4), 513–521.
[25] Hassan, S., Mihalcea, R., Banea, C. (2007). Random-Walk Term Weighting for Improved Text Classification. Semantic Computing, 2007. ICSC 2007. International Conference on (pp. 242–249).
[26] Hou, X., Ong, S. K., Nee, A. Y. C., Zhang, X. T., Liu, W. J. (2011). GRAONTO: A graph-based approach for automatic construction of domain ontology. Expert Systems with Applications, 38(9), 11958–11975.
[27] Jiang, X., Tan, A. H. (2009). Learning and inferencing in user ontology for personalized Semantic Web search. Information Sciences, 179(16), 2794–2808.
[28] Jiayi, P., Cheng, C. P. J., Lau, G. T., Law, K. H. (2008). Utilizing Statistical Semantic Similarity Techniques for Ontology Mapping — with Applications to AEC Standard Models. Tsinghua Science & Technology, 13, Supplement 1(0), 217–222.
[29] Kaza, S., Chen, H. (2008). Evaluating ontology mapping techniques: An experiment in public safety information sharing. Decision Support Systems, 45(4), 714–728.
[30] Khasawneh, N., Chan, C. C. (2006). 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). IEEE Computer Society.
[31] Kim, W., Choi, D. W., Park, S. (2008). Agent based intelligent search framework for product information using ontology mapping. J. Intell. Inf. Syst., 30(3), 227–247.
[32] Lai, L. F. (2007). A knowledge engineering approach to knowledge management. Information Sciences, 177(19), 4072–4094.
[33] Lee, C. S., Kao, Y. F., Kuo, Y. H., Wang, M. H. (2007). Automated ontology construction for unstructured text documents. Data Knowl. Eng., 60(3), 547–566.
[34] 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
[35] Lee, J., Park, J. H., Park, M. J., Chung, C. W., Min, J. K. (2010). An intelligent query processing for distributed ontologies. Journal of Systems and Software, 83(1), 85–95.
[36] Li, L., Yang, Y. (2008). Agent-based ontology mapping and integration towards interoperability. Expert Systems, 25(3), 197–220.
[37] Li, Y., Yang, H., Jagadish, H. V. (2006). Term disambiguation in natural language query for XML. Proceedings of the 7th international conference on Flexible Query Answering Systems (pp. 133–146). Milan, Italy: Springer-Verlag.
[38] Li, Z., Yang, M., Ramani, K. (2009). A Methodology for Engineering Ontology Acquisition and Validation. AI EDAM, 23(Special Issue 01), 37–51.
[39] Liu, M., Shen, W., Hao, Q., Yan, J. (2009). An weighted ontology-based semantic similarity algorithm for web service. Expert Systems with Applications, 36(10), 12480–12490.
[40] Lopez, M., Perez, A., Juristo, N. (1997). METHONTOLOGY: from Ontological Art towards Ontological Engineering. Proceedings of the AAAI97 Spring Symposium (pp. 33–40).
[41] Maedche, A., Staab, S. (2001). Ontology learning for the Semantic Web. Intelligent Systems, IEEE, 16(2), 72– 79.
[42] Mao, M., Peng, Y., Spring, M. (2010). An adaptive ontology mapping approach with neural network based constraint satisfaction. Web Semantics: Science, Services and Agents on the World Wide Web, 8(1), 14–25.
[43] Miller, G. A. (1995). WordNet: a lexical database for English. Commun. ACM, 38(11), 39–41.
[44] Nanas, N., Vavalis, M. (2008). A “Bag” or a “Window” of Words for Information Filtering? Proceedings of the 5th Hellenic conference on Artificial Intelligence: Theories, Models and Applications (pp. 182–193). Syros, Greece: Springer-Verlag.
[45] Noy, N., Mcguinness, D. (2001). Ontology Development 101: A Guide to Creating Your First Ontology.
[46] Papadopoulos, A., Lyritsis, A., Manolopoulos, Y. (2008). SkyGraph: an algorithm for important subgraph discovery in relational graphs. Data Mining and Knowledge Discovery, 17(1), 57–76.
[47] Pennerath, F., Napoli, A. (2009). The Model of Most Informative Patterns and Its Application to Knowledge Extraction from Graph Databases. Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II (pp. 205–220). Bled, Slovenia: Springer-Verlag.
[48] Pérez-Agüera, J., Arroyo, J., Greenberg, J., Iglesias, J., Fresno, V. (2010). Using {BM25F} for semantic search. Proceedings of the 3rd International Semantic Search Workshop (pp. 1–8). Raleigh, North Carolina: ACM.
[49] Pirró, G., Talia, D. (2010). UFOme: An ontology mapping system with strategy prediction capabilities. Data & Knowledge Engineering, 69(5), 444–471.
[50] Plessers, P., De Troyer, O., Casteleyn, S. (2007). Understanding ontology evolution: A change detection approach. Web Semantics: Science, Services and Agents on the World Wide Web, 5(1), 39–49.
[51] Porter, M. F. (1997). An algorithm for suffix stripping. In K. S. Jones P. Willett (Eds.), Readings in information retrieval (pp. 313–316). Morgan Kaufmann Publishers Inc.
[52] Qazvinian, V., Abolhassani, H., Haeri (Hossein), S. H., Hariri, B. B. (2008). Evolutionary coincidence-based ontology mapping extraction. Expert Systems, 25(3), 221–236.
[53] Rajpathak, D., Chougule, R. (2011). A generic ontology development framework for data integration and decision support in a distributed environment. Int. J. Comput. Integr. Manuf., 24(2), 154–170.
[54] Ribes, D., Bowker, G. C. (2009). Between meaning and machine: Learning to represent the knowledge of communities. Information and Organization, 19(4), 199–217.
[55] Richards, D. (2004). Addressing the Ontology Acquisition Bottleneck Through Reverse Ontological Engineering. Knowl. Inf. Syst., 6(4), 402–427.
[56] Robert Gaizauskas, Yorick Wilks. (1998). Information Extraction: Beyond Document Retrieval. Computational Linguistics and Chinese Language Processing, 3(2), 17–60.
[57] Robertson, S., Zaragoza, H., Taylor, M. (2004). Simple BM25 extension to multiple weighted fields. Proceedings of the thirteenth ACM international conference on Information and knowledge management (pp. 42–49). Washington, D.C., USA: ACM.
[58] Sánchez, D. (2010). A methodology to learn ontological attributes from the Web. Data Knowl. Eng., 69(6), 573–597.
[59] Salton, G., Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Inf. Process. Manage., 24(5), 513–523.
[60] Shamsfard, M., Barforoush, A. A. (2004). Learning ontologies from natural language texts. International Journal of Human-Computer Studies, 60(1), 17–63.
[61] Sinha, R., Mihalcea, R. (2007). Unsupervised Graph-based Word Sense Disambiguation Using Measures of Word Semantic Similarity. IEEE International Conference on Semantic Computing (ICSC 2007).
[62] Sowa, J. (1999). Building, sharing, and merging ontologies. TutorialS 1 sn, 3–41.
[63] Staab, S., Studer, R., Schnurr, H. P., Sure, Y. (2001). Knowledge Processes and Ontologies. IEEE Intelligent Systems, 16(1), 26–34.
[64] Stanojević, M., Vraneš, S. (2007). Knowledge representation with SOUL. Expert Systems with Applications, 33(1), 122–134.
[65] Tho, Q. T., Hui, S. C., Fong, A. C. M., Cao, T. H. (2006). Automatic Fuzzy Ontology Generation for Semantic Web. IEEE Trans. on Knowl. and Data Eng., 18(6), 842–856.
[66] Toutanova, K., Klein, D., Manning, C., Singer, Y. (2003). Feature-rich part-of-speech tagging with a cyclic dependency network. NAACL (pp. 173–180). Edmonton, Canada: Association for Computational Linguistics.
[67] Tury, M., Bielikov, M. (2006). An approach to detection ontology changes. Workshop proceedings of the sixth international conference on Web engineering (p. 14). Palo Alto, California: ACM.
[68] Valarakos, A. G., Karkaletsis, V., Alexopoulou, D., Papadimitriou, E., Spyropoulos, C. D., Vouros, G. (2006). Building an allergens ontology and maintaining it using machine learning techniques. Computers in Biology and Medicine, 36(10), 1155–1184.
[69] Valencia-García, R., Fernández-Breis, J. T., Ruiz-Martínez, J. M., García-Sánchez, F., Martínez-Béjar, R. (2008). A knowledge acquisition methodology to ontology construction for information retrieval from medical documents. Expert Systems, 25(3), 314–334.
[70] van Dongen, S. (2000, 29). Graph Clustering by Flow Simulation. University of Utrecht.
[71] Weng, S. S., Tsai, H. J., Liu, S. C., Hsu, C. H. (2006). Ontology construction for information classification. Expert Systems with Applications, 31(1), 1–12.
[72] Yan, X., Han, J. (2002). gSpan: Graph-based substructure pattern mining.
[73] Yao, Y., Zeng, Y., Zhong, N., Huang, X. (2007). Knowledge Retrieval (KR). Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence (pp. 729–735). IEEE Computer Society.
[74] Chen, Y. J., Chen, Y. M., Huang, C. Y., Concept Feature-based Ontology Construction and Maintenance, International Journal of Innovative Computing, Information and Control, Volume 7, Number 8, August 2011.
[75] Zhou, L. (2007). Ontology learning: state of the art and open issues. Information Technology and Management, 8(3), 241–252.3
[76] Retrieved May 10, 2012, from http://www.sciencedirect.com/
[77] 趙中岳，個人化知識搜尋與推薦之領域知識本體調適機制，民國99年