臺灣博碩士論文加值系統

隨著資訊科技的快速發展，如何迅速的取得真正需要的相關資料，並從資料中找尋隱含的知識訊息，也構成了知識地圖的價值。然而很少研究探討如何建構最佳化物件座標的知識地圖。此外，過多的參數設定與資料呈現會導致建構過程複雜化且無法直覺了解知識內容。為了解決上述問題本研究提出一個新的自動化的方法來建構知識地圖。本文提出一個基於多維矩陣進入二維空間的方法，以了解多維度中，重要主題之間的複雜關係並自動化呈現於知識地圖。首先，將資料庫非結構化文件轉為結構化文件，以向量空間模組(VSM)的方式來呈現每一篇論文和每一個關鍵字。在利用資料索引技術，包含了特徵詞擷取和特徵詞的權重，接著導入階層式分群法加以分類並找出適合的主題數以建立知識地圖。最後利用多維度量尺法，將主題之多維向量關係轉換至二維空間，同時透過蜂群演算法決定較佳的多維度量尺法之轉換調節矩陣，以保留物件轉換前後的關聯性，最後以轉換後的二維空間建立知識地圖。根據最後實驗結果，適合的分群數是很重要的且會影響到主題知識地圖的視覺觀感。本研究也進行蜂群演算法的參數設定實驗，並給予其建議。最後，我們利用主題知識地圖去觀察2011年至2016年的物聯網之論文主題趨勢分析。

With the rapid development of the information technology, information overload is becoming a serious problem during the information acquisition process. Information overload leads users spend more time to find necessary knowledge. To relieve this difficulty, knowledge map is a systematic approach to reveal the underlying relationships between abundant knowledge sources. However, few studies focused on optimizing the coordinates of objects in the map. In addition, too many parameters should be set which lead them complicated and not intuition. To solve the above problems, this thesis presents a novel knowledge map approach to transform high-dimensional objects into a 2-dimensional space to help understand complicated relatedness among high-dimensional important topics. First, the papers related to certain domains are collected from the knowledge database and papers as the knowledge items that contains many keywords. Second, the collected knowledge items are presented as the vector space model (VSM). In VSM, keywords can be represented as a term vector in m-dimensional space where the term frequency-inverse document frequency (TF-IDF) approach is used for term weighting so that the tf-idf value increases proportionally to the number of times a keyword appears in the knowledge item. Third, hierarchical clustering is used find important topics. Additionally, high-dimensional relationships among objects are transformed into a 2-dimensional space using the multi-dimension scaling method. The optimal transformation coordinate matrix is also determined by using the artificial bee colony (ABC) algorithm. Then, this transformation coordinate matrix is used to construct a two-dimensional knowledge map so that the relationship among all important topics can be visualized easily. According to experiments, it is found that setting appropriate number of clusters is important for visual perception in the knowledge map. In addition, population size and iteration number in ABC algorithm can affect the results. This paper also shows the example of using the proposed topic knowledge map for research trend analysis in IOT during years 2011 to 2016.

Chapter 1 Introduction 12
1.1 Research Background 12
1.2 Research problem 13
1.3 Research objective 14
1.4 Thesis Organization 15
Chapter 2 Related Methods 16
2.1 Types of knowledge maps 16
2.2 Topic map 17
2.2.1 Applications 17
2.2.2 Topics selection by using clustering techniques 19
2.3 Artificial Bee Colony 20
2.3.1 Comparisons 20
2.3.2 Applications 22
2.4 Topics naming by using specialist literature 24
Chapter 3 Research Method 26
3.1 Research framework 26
3.2 Documents collection 28
3.3 Knowledge representation 28
3.4 Hierarchical clustering algorithms 31
3.5 Knowledge map construction with bee colony algorithm 39
3.5.1 MDS transformation 39
3.5.2 Objective functions 44
3.5.3 Artificial Bee Colony (ABC) 45
Chapter 4 Implementation and experiment results 50
4.1 Data collection 50
4.2 Knowledge map construction process 51
4.2.1 Text preprocessing 51
4.2.5 Knowledge label naming 65
4.3 Experimental Designs 70
4.3.1 The threshold values in clustering algorithm 70
4.3.2 Analysis of artificial bee colony (ABC) algorithm parameter setting 76
4.4 Trend analysis for IOT topics 89
Chapter 5 Conclusions and Future work 95
5.1 Conclusions 95
5.2 Future work 96
References 98

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