臺灣博碩士論文加值系統

目前建構本體論的工具與方法很多，而建構出來的本體論也會有所差異，在相同的領域下，如果直接使用已被建構好的本體論，可以節省相當多建構本體論的成本，而在相同領域下，本體論的整合便成為一項重要課題。本篇論文主要提出，由上而下以模糊正規概念分析為基礎及擷取WordNet資訊的本體論整合方法，稱為FFCA-Merge (Fuzzy Formal Concept Analysis-Merge)。此架構的流程是先將需整合的本體論進行預先處理，並從WordNet內容中獲取資訊進行合併及校正，再者利用模糊概念分析技術來彌補WordNet無法完成的合併及校正工作，最後產生模糊本體論，目前領域本體論的合併方法主要還是針對不具有模糊概念的本體論，而透過FFCA-Merge合併之後，可以轉換成模糊本體論。在處理不明確資料時，模糊本體論具有更強大的彈性與優勢。最後在實驗的部份也可證明我們提出的方法確實具有效用。

Many different contents and structures exist in constructed ontologies, including those that exist in the same domain. If extant domain ontologies can be used, time and money can be saved. However, domain knowledge changes fast. In addition, the extant domain ontologies may require updates to solve domain problems. The reuse of extant ontologies is an important topic for their application. Thus, the integration of extant domain ontologies is of considerable importance. In this paper, we propose a new method for combining the WordNet and Fuzzy Formal Concept Analysis (FFCA) techniques for merging ontologies with the same domain, called FFCA-Merge. Through the method, two extant ontologies can be converted into a fuzzy ontology. The new fuzzy ontology is more flexible than a general ontology. The experimental results indicate that our method can merge domain ontologies effectively.

摘要 III
Abstract IV
致謝 V
Table of Contents VII
List of Tables X
List of Figures XI
Chapter 1 Introduction 1
1.1 Background and Motivation 1
1.2 Objective 2
1.3 Thesis Framework 2
Chapter 2 Literature Review 3
2.1 Ontology and Fuzzy Ontology 3
2.2 Ontology Mapping Categories 4
2.3 Survey on Domain Ontology Merge and Alignment 5
2.4 Related Techniques 8
2.4.1 WordNet 9
2.4.2 Formal Concept Analysis (FCA) 10
2.4.3 Fuzzy Formal Concept Analysis (FFCA) 14
2.4.4 FCA-Merge 17
Chapter 3 Ontology Merging for Fuzzy Ontology Based on WordNet and FCA Techniques 19
3.1 Pre-processing 20
3.2 Ontology Merge Phase 22
3.2.1 Class Name Merging 23
3.2.2 WordNet Alignment 24
3.2.3 FFCA Alignment 28
3.2.4 Generation Fuzzy Ontology 34
Chapter 4 Experiments and Discussions 38
4.1 Experimental Environment and Data Sources 38
4.2 Comparisons of Each Element 44
4.2.1 Influence of Number of Documents 44
4.2.2 Influence of Direct Mapping and Lattice Calculation Strategies 47
4.2.3 Influence of Different Domains 52
4.3 Experimental Results 52
Chapter 5 Conclusions and Future Works 70
References 72

List of Tables
Table 1 An example of FCA-Context ............................................................................. 13
Table 2 The membership value of FFCA-Context......................................................... 15
Table 3 The FFCA-Context is removed from membership values lower than 0.6 . 16
Table 4 The baseball ontology ......................................................................................... 39
Table 5 The academic ontology....................................................................................... 40
Table 6 The details of FFCA-Merge ............................................................................... 41
Table 7 The number of concepts necessarily merged or aligned .................................. 42
Table 8 The summary of number of documents for the baseball ontology .................. 46
Table 9 The summary of number of documents for the academic ontology ................ 47
Table 10 The summary of two strategies for the baseball ontology ............................. 50
Table 11 The summary of two strategies for the academic ontology ........................... 51
Table 12 The summary of experimental results for the baseball ontology ................... 54
Table 13 The summary of experimental results for the academic ontology................. 55
Table 14 The relationships between expert concepts and system keywords ................ 57
Table 15 Evaluate the merging results ............................................................................ 59
Table 16 The relationships of two ontologies ................................................................. 64

List of Figures
Figure 1 An example of ontology and fuzzy ontology ..................................................... 4
Figure 2 An example of WordNet contents .................................................................... 10
Figure 3 A FCA-Concept lattice ...................................................................................... 14
Figure 4 The FFCA-Concept lattice got from Table 3................................................... 16
Figure 5 A flow chart of FCA-Merge ............................................................................. 17
Figure 6 The workflow of FFCA with WordNet ........................................................... 20
Figure 7 The framework of pre-processing .................................................................... 22
Figure 8 Sibling relationship ............................................................................................ 26
Figure 9 An example of class-name merging and FFCA mapping .............................. 27
Figure 10 The workflow of FFCA alignment ................................................................ 30
Figure 11 An example of calculation similarity ............................................................. 32
Figure 12 An example of a translation concept lattice ................................................... 33
Figure 13 The information in our fuzzy ontology .......................................................... 36
Figure 14 An example of our fuzzy ontology ................................................................. 37
Figure 15 Number of documents for the baseball ontology .......................................... 45
Figure 16 Number of documents for the academic department ontology .................... 46
Figure 17 DMS and LCS strategies for the baseball domain ........................................ 49
Figure 18 DMS and LCS strategies for the academic department domain .................. 49
Figure 19 The result of FFCA alignment (Baseball Domain) ....................................... 53
Figure 20 The result of FFCA alingment (Academic Domain) .................................... 53
Figure 21 The concept lattice of a baseball ground (baseball ontology) ...................... 60
Figure 22 The concept lattice of a staff on level 2 (academic department) .................. 60
Figure 23 The concept lattice of a staff on level 3 (academic department) .................. 61
Figure 24 The concept lattice of a teaching faculty on level 2 (academic department) . 61
Figure 25 The concept lattice of a teaching faculty on level 3 (academic department) . 62
Figure 26 Baseball Domain Ontology_Base Ontology ................................................. 66
Figure 27 Baseball Domain Ontology_Revision Ontology........................................... 67
Figure 28 Academic Department Domain Ontology_Base Ontology.......................... 68
Figure 29 Academic Position Domain Ontology_Revision Ontology ......................... 69

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