在這資訊爆炸的時代，人類所需要處理的資訊，已經遠超過我們的負荷，因此，將一些相當花費人力與時間的工作交由電腦自動化處理，一直是我們所追求的目標之一。
隨著數位學習領域的標準趨向統一化，大部分的數位教材內容、學習元件(Learning Object)皆是以IEEE所制定的「學習物件後設資料」（learning objects metadata; LOM）來描述學習元件。我們可以輕易的在網路上搜尋到許多符合國際標準的學習元件，而這些學習元件可以一再的被不同的教學者重組、再利用。因此，若能提供一個自動化的學習元件分類機制，將這些學習元件自動的分類到適合的類別，將有助於教學者更快速的將學習教材重組、再利用。雖然在資料探勘(Data Mining)的領域中，有許多自動化文件分類的技術，然而這些傳統的文件分類技術，並不是專門對學習元件所設計的，因此本論文提出一個基於本體論的自動化學習元件分類演算法，演算法主要著重在分析LOM (Learning Object Metadata)欄位的特性，擷取出LOM中有助於判斷分類的關鍵詞(Term)，並配合領域專家所建立的本體論(Ontology)，將學習元件做自動化的分類。

In the age of information explosion, the number of information people have to digest or deal with is over the edge of their tolerance. To hand over some manpower-consuming tasks to computers is one of the goals people pursue, which is also quite true in e-learning paradigm.
With the standard convergence in the e-learning, most of the learning contents and learning objects are described by learning object metadata (LOM) that IEEE formulating. People can search easily from internet repositories and fetch many learning objects with standard LOMs. These learning objects can then be recombined and reused in different occasions. Therefore, if an automatic method for learning objects classification is available which groups learning objects into appropriate assortment, the jobs of recombination and reusing can be done quickly. In data mining, while there are many techniques for automatic classification, they are not suitable for automatic learning object classifications.
This study proposes an ontology-based automatic learning object clas¬si¬fi¬cation algorithm. This algorithm focuses on analyzing the character¬istics of learning object metadata (LOM) and retrieving terms from LOM which help on classification. The power of the automatic classification of the algorithm comes from an ontology that domain expert constructed to guide the process of classification automatically.

[1] Advanced Distributed Learning (ADL) initiative, “Sharable Content Object Reference Model (SCORM)”, http://www.adlnet.org/
[2] Advanced Distributed Learning (ADL) initiative, “SCORM Specifications – The SCORM Content Aggregation Model Version 1.2”, October 1, 2001.
[3] Advanced Distributed Learning (ADL) initiative, “SCORM Specifications – The SCORM Run-Time Environment Version 1.2”, October 1, 2001.
[4] Advanced Distributed Learning (ADL) initiative, “SCORM Specifications – SCORM Version 1.3 Application Profile Working Draft Version 1.0”, March 26, 2003.
[5] ARIADNE (1998). "The Alliance of Remote Instructional Authoring and Distribution networks for Europe", http://ariadne.unil.ch/.
[6] Aviation Industry CBT Committee (AICC), “Computer Managed Instruction (CMI)”, http://www.aicc.org/.
[7] Commonwealth of Learning - Learning Object Repository, http://www.col.org/colweb/site/pid/2922.
[8] D.Brickley and R.Guha, “Resource Description Framework(RDF) Schema Specification,”W3C Candidate Recommendation, Mar. 2000; available at http://www.w3.org/TR/2000/CR-RDF-schema-20000327.
[9] D. Fensel, F. van Harmelen, Ian Horrocks, D. L. Mcguinness, and P. F. Patel-Schnedier, “OIL: An Ontology Infrastructure for the Semantic Web”, IEEE Intelligent System, Vol.16, no.2, pp.38-45, March/April 2001.
[10] F. Sebastiani, “Machine Learning in Automated Text Categorization”, ACM Computing Surveys, Vol.34, No.1, March 2002, pp.1-47.
[11] G. Salton, M. McGill, ”Introduction to Modern Information Retrieval,” McGraw-Hill New York, 1983.
[12] H. Cui, J. R. Wen, J. Y. Nie, and W. Y. Ma, “Query Expansion by Mining User Logs”, IEEE Transaction on Knowledge and Data Engineering. Vol.15, no.4, pp.829-839, July/August 2003.
[13] IEEE Learning Technology Standards Committee (LTSC), http://ltsc.ieee.org
[14] I. Horrocks, D. Fensel, J. Broekstra, S. Decker, M. Erdmann, C. Grble, and F. van Harmelen, “The Ontology Interface Layer OIL”, August 2000. Available at：http://www.ontokowledge.org.
[15] Jena – A Semantic Web Framework for Java , http://jena.sourceforge.net/
[16] IMS Global Learning Consortium, Inc., “Instructional Management System (IMS)”, http://www.imsglobal.org.
[17] K. Aas and L. Eikvil. “Text categorisation: A survey. Technical report”, Norwegian Computing Center, June 1999.
[18] L. Khan, D. McLeod and E. Hovy, “Retrieval Effectiveness of Ontology-based Model for Information Selection” the VLDB Journal: The International Journal on Very Large Databases, ACM/Springer-Verlag Publishing, Vol. 13(1): 71-85 (2004).
[19] L. Khan, “Ontology-based Information Selection” Ph.D. Dissertation, Department of Computer Science, University of Southern California, August 2000.
[20] M. C. Lee, D. Y. Ye, and T. I. Wang, ”Java Learning Object Ontology”, The 5th IEEE International Conference on Advanced Learning Technologies, pp.538-542, July 2005, Kaohsiung, Taiwan.
[21] M. C. Lee, T. K. Chiu, K. H. Tsai, and T. I. Wang, “An Ontological Approach for Semantic-Aware Learning Object Retrieval”, The 6th IEEE International Conference on Advanced Learning Technologies, pp.208-210, July 2006, Kerkrade, Netherland.
[22] N.F.Noy and D.L.Mcguinness, ”Ontology Development 101: A Guide to Creating Your First Ontology,” Stanford Knowledge System Laboratory Technical Report KSL-01-05 and Stanford Medical Informatics Technical Report SMI-2001-0880,Mar.2001.
[23] O.Lassila and R.Swick, ”Resource Description Framework(RDF) Model and Syntax Specification,”World Wide Web Consortium Recommendation, Feb. 1999; available at http://www.w3.org/TR/REC-rdf-syntax/.
[24] Ontology Markup Language Version 0.3 , http://www.ontologos.org/OML/OML%200.3.htm.
[25] OWL Web Ontology Language Overview, http://www.w3.org/TR/2004/REC-owl-features-20040210/.
[26] P. Pantel, D. Lin. Discovering Word Senses from Text. In Proceedings of ACM SIGKDD Conference on Knowledge Discovery and Data Mining 2002:613-619.
[27] R. Karp, V. Chaudhri, and J. Thomere, “XOL: An XML-based Ontology Exchange Language,”Technical Report, Aug. 1999.
[28] SCORM (Sharable Courseware Object Reference Model), http://www.adlnet.org.
[29] SMETE Digital Library, http://www.smete.org/smete/.
[30] T. Berner-Lee, J.Hendler, and O.Lassila. The Semantic Web. Scientific Ammerican, 279, 2001.
[31] The ACM Computing Classification System [1998 Version], http://www1.acm.org/class/1998/.
[32] The Java Tutorials , http://java.sun.com/docs/books/tutorial/.
[33] The Porter Stemming Algorithm, http://www.tartarus.org/martin/PorterStemmer/.
[34] The Protege Ontology Editor and Knowledge Acquisition System, http://protege.stanford.edu/.
[35] T.R. Gruber. “A translation approach to portable ontolog specications,”Knowledge Acquisition,vol.5,issue 2,pp.199-220.1993.
[36] Welcome to Xerces, http://xerces.apache.org/.
[37] 王常威, “以內容為基礎之XML文件分類方法之研究” , 成功大學資訊管理研究所, 2003.
[38] 高志強, “組合自動化文件分類技術之研究-以專利文件分類為例” , 中原大學資訊管理研究所, 2003.
[39] 林照庭, “辭書式教材分類系統之建置 – 以符合SCORM規範之基本電學教材為例” , 成功大學工程科學研究所, 2004.
[40] 蔡俊彥, “符合SCORM規範教材庫管理系統之研究” , 高雄師範大學資訊教育研究所, 2002.
[41] 鐘明強, “基於Ontology架構之文件分類網路服務研究與建構” ,成功大學資訊工程研究所, 2002.
[42] 黎炯良, “辭書間之自動化對映機制” , 成功大學工程科學研究所, 2005.