隨著網際網路與知識經濟環境的發展，使得人們以及組織可以快速地在網路上產生並擷取大量所需資訊，其中大部分為文字格式的文件，因此，對於電子化文件管理的需求也隨之增加。為了處理這些大量增加的文件，人們習慣用類別或文件夾的概念來整理其檔案或文件。除此之外，每個人對於分類往往有不同的標準與偏好，因而形成不同的文件群集。基於自動化文件管理的需求以及個人化概念的重要性，在我們的研究中，提出了一個以本體論為基礎之文件分群技術(OnPEC)，採用個人的部份分群(partial clustering)資訊以及一個本體論(ontology)來幫助我們達成個人化的文件分群。其中，個人的部份分群可以當成擷取個人的分類偏好的來源，而採用本體論可以將文件分群技術由特徵值的基礎(feature-based)提升至概念的基礎(concept-based)。同時，本論文對分群過程採用atomic-based HAC與pre-cluster-based HAC二種方法。以傳統的文件分群技術及先前學者所提之特徵值為基礎的個人化文件分群技術(PEC)做為分群效能比較基準，本研究實證結果顯示，採用個人部份分群資訊的文件分群技術能夠更接近個人分群的結果。此外，不管在OnPEC以及PEC的方法中，pre-cluster-based HAC比起atomic-based HAC都有較優異的分群表現。

With the proliferation of electronic commerce and knowledge economy environments, both persons and organizations increasingly have generated and consumed large amounts of online information, typically available as textual documents. To manage this rapid growth of the number of textual documents, people often use categories or folders to organize their documents. These document grouping behaviors are intentional acts that reflect the persons’ (or organizations’) preferences with regard to semantic coherency, or relevant groupings between subjects. For this thesis, we design and implement an ontology-based personalized document clustering (OnPEC) technique by incorporating both an individual user’s partial clustering and an ontology into the document clustering process. Our use of a target user’s partial clustering supports the personalization of document categorization, whereas our use of the ontology turns document clustering from a feature-based to a concept-based approach. In addition, we combine two hierarchical agglomerative clustering (HAC) approaches (i.e., pre-cluster-based and atomic-based) in our proposed OnPEC technique. Using the clustering effectiveness achieved by a traditional content-based document clustering technique and previously proposed feature-based document clustering (PEC) techniques as performance benchmarks, we find that use of partial clusters improves document clustering effectiveness, as measured by cluster precision and cluster recall. Moreover, for both OnPEC and PEC techniques, the clustering effectiveness of pre-cluster-based HAC methods greatly outperforms that of atomic-based HAC methods.

CHAPTER 1 INTRODUCTION 1
1.1 BACKGROUND 1
1.2 RESEARCH MOTIVATIONS AND OBJECTIVE 2
1.3 ORGANIZATION OF THE THESIS 5
CHAPTER 2 LITERATURE REVIEW 6
2.1 GENERAL PROCESS OF CONTENT-BASED DOCUMENT CLUSTERING 6
2.2 PERSONALIZED DOCUMENT CLUSTERING 11
2.4 OVERVIEW OF ONTOLOGY 16
2.5 ONTOLOGY-BASED TEXT CLUSTERING 19
CHAPTER 3 DESIGN OF ONTOLOGY-BASED PERSONALIZED DOCUMENT CLUSTERING (OnPEC) TECHNIQUE 22
3.1 ONTOLOGY LEARNING 23
3.1.1 Feature Extraction 23
3.1.2 Concept Feature Selection 24
3.2 ONTOLOGY-BASED PERSONALIZED DOCUMENT CLUSTERING PROCESS 26
3.2.1 Feature Extraction 27
3.2.2 Concept Mapping 27
3.2.3 Concept Selection 27
3.2.4 Concept-Based Document Representation 30
3.2.5 Clustering 30
CHAPTER 4 EMPIRICAL EVALUATION OF THE ONTOLOGY-BASED PERSONALIZED DOCUMENT CLUSTERING (OnPEC) TECHNIQUE 32
4.1 EVALUATION DESIGN 32
4.1.1 Document Corpus and Personal Categorization Collection 32
4.1.2 Document Corpus and Concept Hierarchy for Ontology Learning 33
4.1.3 Evaluation Criteria 35
4.1.4 Evaluation Procedure 36
4.2 TUNING EXPERIMENTS 37
4.2.1 Effects of Number of Features in Benchmark Techniques 37
4.2.2 Parameter Tuning for the OnPEC Technique 39
4.3 COMPARATIVE EVALUATION RESULTS 42
4.4 SENSITIVITY OF THE SIZE OF PARTIAL CLUSTERS FOR CLUSTERING EFFECTIVENESS 45
CHAPTER 5 CONCLUSIONS AND FUTURE RESEARCH DIRECTION 50
REFERENCES 52
APPENDIX A: ACM COMPUTING CLASSIFICATION SYSTEM (CCS), 1998 56
APPENDIX B: ACM CCS CONCEPT HIERARCHY IN EMPIRICAL EVALUATION 67

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