臺灣博碩士論文加值系統

文獻內容發展趨勢已相當成熟並且過於龐大，要了解相關領域研究的知識結構，則需要透過相關描述知識工具，如:引用文獻分析法(Citation analysis)，透過引用文獻分析法了解該領域的主要研究議題與內涵並且了解相關領域議題中各研究議題間的相對關係，學術的價值會根據該研究學者對於該研究的重視度、投入程度而有所差異。傳統的共被引分析並無考慮到共被引的兩篇被引文獻再文章中的位置，透過傳統的共被引方式只能從被引用的頻率次數來判別該學術研究之貢獻程度，會造成部分被引文獻的學術貢獻價值遺漏。
本研究透過引用文獻分析法當中的共被引分析法(co-citation analysis)建構特定領域的知識結構，以生物醫學資訊學領域文章作為分析對象，並且別於傳統之共引分析法，提出針對引用內容的相似性評估方法，評估共被引關係，並且更細部探討文章中各章節的引用位置狀態，針對被引用在不同章節的分布，賦予該被引文獻不同之貢獻價值，透過分析結果與領域知識流(Knowledge Flow)的脈絡，詮釋文章所帶來的學術價值。

Document content development trend has been quite mature and too large. To understand the knowledge structure to the related research in the field, you would need the relevant knowledge tools, such as Citation analysis, to understand the main research subject and content, as well as understanding the relative relationship topics in various research topics. The academic value will vary according to the degree of importance and level of commitment for the researchers. The traditional co-citation analysis does not take into account the position of where the two co-cited document are cited in the article. The traditional co-citation analysis can determine the contribution level of the academic research through the frequency of the number of times that it was cited, which causing part of the contribution value missing.

In this study, through citation analysis among the co-citation analysis to construct knowledge structure in specific areas and use biomedical informatics field article as an analysis object, and differentiate from the tradition co-citation analysis, we’re able to bring forward the similarity assessment citation methods, assessing co-citation relations, and explore in detail the position status of the cited sections in the article to assign different contribution value. And by analyzing the results with the knowledge flow to interpret the academic value that the article brings.

論文摘要 V
ABSTRACT VI
圖 目 錄 IX
表 目 錄 X
第一章 緒論 1
第一節 研究動機 1
第二節 研究背景 2
第三節 研究目的 3
第四節 研究流程 5
第二章 文獻探討 7
第一節 文獻計量學（Bibliometrics） 7
第二節 引用文獻分析(Citation analysis) 7
第三節 共被引分析 (co-citation analysis) 10
第四節 知識流 (knowledge flow) 19
第三章 研究方法 23
第一節 共被引分析（Co-citation） 23
第二節 社會網路分析(Social Network Analysis) 26
第三節 研究架構 28
第四節 資料蒐集(Pubmed Central) 30
壹、 資料來源 30
貳、 資料格式 32
參、 資料狀態 35
第五節 引用位置與引用內容(Citation location/ Citation content) 37
壹、 引用位置(Citation location) 37
貳、 引用內容(Citation content) 43
第六節 系統實作 44
壹、 文獻處理系統 44
貳、 知識脈絡系統 56
第四章 結果與討論 59
第一節 被引文獻總計量分析 59
第二節 章節層次的被引文獻計量分析 60
壹、章節歸類 60
貳、各章節之引用文獻排名 69
參、引用文獻價值賦予 76
第三節 被引次數計量分析 78
第四節 被引期刊計量分析 80
第五節 共被引分析 81
第六節 社會網路分析 84
第七節 集群分析(Cluster Analysis) 88
第八節 知識脈絡 98
第五章 結論 107
壹、研究限制 109
貳、未來展望 110
第六章 參考文獻 111
附錄一　文獻編號對應表 114
附錄二　章節歸類結果 130
附錄三　共被引矩陣 149

圖 目 錄
圖 1研究流程圖 6
圖 2書目耦合與共被引 25
圖 3 研究架構圖 28
圖 4 引用位置 38
圖 5引用內容 43
圖 6文獻解析系統建置流程 46
圖 7特定標籤內容擷取模組 46
圖 8 　XML 主要結構圖 47
圖 9 引用內容文句擷取模組 49
圖 10 文章XML標籤與引用內容擷取流程圖 50
圖 11 引用內容相似度計算模組 50
圖 12 餘弦相似性圖 52
圖 13 建立針對引用內容的共被引矩陣 53
圖 14 種子層文獻 56
圖 15 知識串流示意圖 57
圖 16 章節架構 69
圖 17 網路圖 84
圖 18 網路圖-2 87
圖 19 集群化合作網路圖 94
圖 20集群化合作網路圖2 95
圖 21 知識脈絡階層 99
圖 22 知識脈絡表 100
圖 23 知識脈絡視窗介面 101
圖 24知識脈絡視窗介面2 102
圖 25知識脈絡視窗介面3 102
圖 26知識脈絡視窗介面4 103
圖 27知識脈絡視窗介面5 104
圖 28知識脈絡視窗介面6 104


表 目 錄

表格 1 xml標籤大體基本結構 32
表格 2 NCBI 定義的XML 元素 34
表格 3 PMC期刊排行 36
表格 4餘弦向量表 52
表格 5 原始共被引矩陣範例 54
表格 6 相關係數共被引矩陣 55
表格 7 引用文獻總數量表 59
表格 8 簡介章節歸類表 61
表格 9 文獻探討章節歸類表 63
表格 10 研究方法章節歸類法 65
表格 11 研究結果與討論章節歸類表 67
表格 12 結論章節歸類表 68
表格 13 簡介章節的引用文獻排名 71
表格 14 文獻探討章節的引用文獻排名 72
表格 15 文獻探討章節的引用文獻排名 73
表格 16結果與討論章節的引用文獻排名 75
表格 17結論章節的引用文獻排名 76
表格 21 排名20文章賦予章節價值 77
表格 18 引用內容與引用位置方法之高度被引用文章列表 79
表格 19 傳統方法之高度被引用文章列表 80
表格 20 引用期刊次數排名 81
表格 22 局部共被引矩陣表 82
表格 23紅色圈-中心度值排名 86
表格 24 集群1-心臟疾病 89
表格 25集群2-長期照護之老人健康管理和失能照顧 90
表格 26集群3-長期照護服務之政策與保險法 90
表格 27集群4-老年人常見疾病 91

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