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題名 人物與機構之知識圖譜分析工具發展與數位人文應用
Development and Application of the Knowledge Graph Analysis Tool of Characters and Institutions on Digital Humanities作者 林俊佑
Lin, Chun-Yu貢獻者 陳志銘
Chen, Chih-Ming
林俊佑
Lin, Chun-Yu關鍵詞 數位人文
人物與機構關係
知識圖譜
文本探勘
機器學習
資訊視覺化
滯後序列分析
Digital Humanities
Relationship between Characters and Institutions
Knowledge Graph
Text Mining
Machine Learning
Information Visualization
Lag Sequence Analysis日期 2021 上傳時間 2-Sep-2021 16:35:40 (UTC+8) 摘要 知識圖譜為語意上資料結構的一種，已被證實有助於數位資訊系統進行更有意義的知識表達，並且憑藉其動態鏈結與視覺化的特性，更有利於知識的引導與吸收。本研究旨在開發支援數位人文研究之「人物與機構之知識圖譜分析工具」，輔以人文學者透過知識圖譜功能的特性，不僅清楚看到人物與人物之間的關聯，更能透過人物與機構關係看到潛在於文本中的人物關係。為了驗證此一工具對於支援數位人文研究的效益，本研究以準實驗研究法之對抗平衡設計比較實驗對象依序使用有無「知識圖譜功能之人物與機構關係分析工具」進行文本人物與機構脈絡探索，在探索成效與效率上是否具有顯著的差異；並以科技接受度問卷、半結構訪談的方式瞭解實驗對象對「人物與機構之知識圖譜分析工具」的看法與感受；最後，透過滯後序列分析搭配螢幕錄影，探討實驗對象操作兩系統時的行為轉移差異。實驗結果發現，相較於採用「無知識圖譜功能之人物與機構關係分析工具」，採用「具知識圖譜功能之人物與機構關係分析工具」更能輔助受測對象在有限的時間內探索文本中的人物與機構關係，提高文本脈絡探索成效。在科技接受度分析得知，受測對象對於「具知識圖譜功能之人物與機構關係分析工具」持正面肯定態度，認為此工具的操作直觀且能幫助到他們進行文本人物與機構脈絡探索。此外，滯後序列分析結果發現，使用「具知識圖譜功能之人物與機構關係分析工具」的受測對象能在短時間內找到有系統性的探索模式，進而完成人物與機構脈絡探索表所賦予的任務。訪談內容分析得知大部分受測對象皆認為知識圖譜工具能做為探索人物與機構關係時很好的探索切入點，提高整體人物脈絡探索的成效與效率，但也有部分受測對象認為知識圖譜工具在一次觀看多位人物時，其圖上呈現的資訊量有點過多，認為知識圖譜呈現功能還有進一步改善空間。在未來研究方向上，可以考慮納入更多實體元素，舉凡地名、時間等充實知識圖譜，並且引入更多南洋人物誌或名人傳記文本充實平台內容。
Knowledge graph is a kind of semantic data structure, which has been proved its benefits in promoting digital information system to carry out more meaningful knowledge representation, and by virtue of its dynamic link and visual characteristics, it is more conducive to knowledge guidance and absorption. This research aims to develop a Knowledge Graph Analysis Tool of Characters and Institutions (KGAT-CI) that can support digital humanities research more effectively. Knowledge graph in the KGAT-CI is provided for humanities scholars so that they not only clearly see the relationship between characters, but also view the potential character relationships through characters and institutional connection. In order to verify the effectiveness of this tool in supporting digital humanities research, a counterbalanced design in the quasi-experimental research was applied in this study to compare the experiment subjects of two groups who respectively used a character digital humanities research platform with and without KGAT-CI to explore the relationship between the two entities of character and institution, and if there were significant differences in the learning effectiveness and efficiency between the two groups. Technology acceptance questionnaire and semi-structured interview were utilized for understanding the experiment subjects’ opinions and perception toward KGAT-CI. Finally, lag sequential analysis and screen recording analysis were used for observing the experiment subjects’ behavior processes using two different systems to discuss whether the notable difference in the operation behavior transfer between the two groups existed.The experimental results show that the KGAT-CI could help the experiment subjects to improve the effectiveness of exploring the relationships between characters and institutions under limited time. The technology acceptance of the experiment subjects with KGAT-CI support reveals highly positive satisfaction. It presents that such a tool could help the experiment subjects explore the connections between characters and institutions. Besides, lag sequential analysis reveals that the experiment subjects who used KGAT-CI could rapidly generalize systematic patterns to explore the relationship between characters and institutions from texts in limited time. The interview reveals that most of the experimental subjects believed the knowledge graph can be a good entry point for exploring the relationship between characters and institutions, improve the effectiveness and efficiency of the overall character network exploration. However, some experimental subjects expressed that the KGAT-CI provides too much information when this tool was used to view multiple characters and institutions at a time. 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Review article. El Profesional de la Información, 28(1). Retrieved from https://doi.org/10.3145/epi.2019.ene.03 描述 碩士
國立政治大學
圖書資訊與檔案學研究所
108155015資料來源 http://thesis.lib.nccu.edu.tw/record/#G0108155015 資料類型 thesis dc.contributor.advisor 陳志銘 zh_TW dc.contributor.advisor Chen, Chih-Ming en_US dc.contributor.author (Authors) 林俊佑 zh_TW dc.contributor.author (Authors) Lin, Chun-Yu en_US dc.creator (作者) 林俊佑 zh_TW dc.creator (作者) Lin, Chun-Yu en_US dc.date (日期) 2021 en_US dc.date.accessioned 2-Sep-2021 16:35:40 (UTC+8) - dc.date.available 2-Sep-2021 16:35:40 (UTC+8) - dc.date.issued (上傳時間) 2-Sep-2021 16:35:40 (UTC+8) - dc.identifier (Other Identifiers) G0108155015 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/136925 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 圖書資訊與檔案學研究所 zh_TW dc.description (描述) 108155015 zh_TW dc.description.abstract (摘要) 知識圖譜為語意上資料結構的一種，已被證實有助於數位資訊系統進行更有意義的知識表達，並且憑藉其動態鏈結與視覺化的特性，更有利於知識的引導與吸收。本研究旨在開發支援數位人文研究之「人物與機構之知識圖譜分析工具」，輔以人文學者透過知識圖譜功能的特性，不僅清楚看到人物與人物之間的關聯，更能透過人物與機構關係看到潛在於文本中的人物關係。為了驗證此一工具對於支援數位人文研究的效益，本研究以準實驗研究法之對抗平衡設計比較實驗對象依序使用有無「知識圖譜功能之人物與機構關係分析工具」進行文本人物與機構脈絡探索，在探索成效與效率上是否具有顯著的差異；並以科技接受度問卷、半結構訪談的方式瞭解實驗對象對「人物與機構之知識圖譜分析工具」的看法與感受；最後，透過滯後序列分析搭配螢幕錄影，探討實驗對象操作兩系統時的行為轉移差異。實驗結果發現，相較於採用「無知識圖譜功能之人物與機構關係分析工具」，採用「具知識圖譜功能之人物與機構關係分析工具」更能輔助受測對象在有限的時間內探索文本中的人物與機構關係，提高文本脈絡探索成效。在科技接受度分析得知，受測對象對於「具知識圖譜功能之人物與機構關係分析工具」持正面肯定態度，認為此工具的操作直觀且能幫助到他們進行文本人物與機構脈絡探索。此外，滯後序列分析結果發現，使用「具知識圖譜功能之人物與機構關係分析工具」的受測對象能在短時間內找到有系統性的探索模式，進而完成人物與機構脈絡探索表所賦予的任務。訪談內容分析得知大部分受測對象皆認為知識圖譜工具能做為探索人物與機構關係時很好的探索切入點，提高整體人物脈絡探索的成效與效率，但也有部分受測對象認為知識圖譜工具在一次觀看多位人物時，其圖上呈現的資訊量有點過多，認為知識圖譜呈現功能還有進一步改善空間。在未來研究方向上，可以考慮納入更多實體元素，舉凡地名、時間等充實知識圖譜，並且引入更多南洋人物誌或名人傳記文本充實平台內容。 zh_TW dc.description.abstract (摘要) Knowledge graph is a kind of semantic data structure, which has been proved its benefits in promoting digital information system to carry out more meaningful knowledge representation, and by virtue of its dynamic link and visual characteristics, it is more conducive to knowledge guidance and absorption. This research aims to develop a Knowledge Graph Analysis Tool of Characters and Institutions (KGAT-CI) that can support digital humanities research more effectively. Knowledge graph in the KGAT-CI is provided for humanities scholars so that they not only clearly see the relationship between characters, but also view the potential character relationships through characters and institutional connection. In order to verify the effectiveness of this tool in supporting digital humanities research, a counterbalanced design in the quasi-experimental research was applied in this study to compare the experiment subjects of two groups who respectively used a character digital humanities research platform with and without KGAT-CI to explore the relationship between the two entities of character and institution, and if there were significant differences in the learning effectiveness and efficiency between the two groups. Technology acceptance questionnaire and semi-structured interview were utilized for understanding the experiment subjects’ opinions and perception toward KGAT-CI. Finally, lag sequential analysis and screen recording analysis were used for observing the experiment subjects’ behavior processes using two different systems to discuss whether the notable difference in the operation behavior transfer between the two groups existed.The experimental results show that the KGAT-CI could help the experiment subjects to improve the effectiveness of exploring the relationships between characters and institutions under limited time. The technology acceptance of the experiment subjects with KGAT-CI support reveals highly positive satisfaction. It presents that such a tool could help the experiment subjects explore the connections between characters and institutions. Besides, lag sequential analysis reveals that the experiment subjects who used KGAT-CI could rapidly generalize systematic patterns to explore the relationship between characters and institutions from texts in limited time. The interview reveals that most of the experimental subjects believed the knowledge graph can be a good entry point for exploring the relationship between characters and institutions, improve the effectiveness and efficiency of the overall character network exploration. However, some experimental subjects expressed that the KGAT-CI provides too much information when this tool was used to view multiple characters and institutions at a time. In the future research directions, it is able to including more physical elements, such as location, time, etc. to enrich the knowledge graph in the KGAT-CI, and import more Southeast Asia characters or celebrity biographies to enrich the character digital humanities research platform. en_US dc.description.tableofcontents 目次第一章 緒論 1第一節 研究背景與動機 1第二節 研究目的 4第三節 研究問題 5第四節 研究範圍與限制 6第五節 名詞解釋 7第二章 文獻探討 10第一節 數位人文 10第二節 知識圖譜 13第三節 馬來西亞華人歷史研究 16第三章 系統設計 20第一節 系統設計理念 20第二節 系統架構 21第三節 系統元件 24第四節 系統開發環境 28第五節 系統介面與功能 29第四章 研究設計與實施 34第一節 研究架構 34第二節 研究方法 38第三節 研究對象 39第四節 研究工具 40第五節 實驗設計 43第六節 資料與分析 46第七節 研究實施步驟 49第五章 實驗結果分析 51第一節 實驗對象之基本資料分析 52第二節 兩系統輔以人物脈絡探索之成效差異分析 54第三節 兩系統輔以完成人物脈絡探索之效率差異分析 56第四節 兩系統輔以人物脈絡探索之科技接受度差異分析 57第五節 兩系統輔以人物脈絡探索之使用者行為歷程分析 59第六節 訪談資料分析 72第七節 綜合討論 80第六章 結論與建議 84第一節 結論 84第二節 工具改善建議 88第三節 未來研究方向 90參考文獻 91附錄一 學習單-經濟面向 96附錄二 學習單-文教面向 98附錄三「人物與機構關係之知識圖譜分析工具」科技接受度 100附錄四「閱讀文本介面」科技接受度 102 zh_TW dc.format.extent 3752323 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0108155015 en_US dc.subject (關鍵詞) 數位人文 zh_TW dc.subject (關鍵詞) 人物與機構關係 zh_TW dc.subject (關鍵詞) 知識圖譜 zh_TW dc.subject (關鍵詞) 文本探勘 zh_TW dc.subject (關鍵詞) 機器學習 zh_TW dc.subject (關鍵詞) 資訊視覺化 zh_TW dc.subject (關鍵詞) 滯後序列分析 zh_TW dc.subject (關鍵詞) Digital Humanities en_US dc.subject (關鍵詞) Relationship between Characters and Institutions en_US dc.subject (關鍵詞) Knowledge Graph en_US dc.subject (關鍵詞) Text Mining en_US dc.subject (關鍵詞) Machine Learning en_US dc.subject (關鍵詞) Information Visualization en_US dc.subject (關鍵詞) Lag Sequence Analysis en_US dc.title (題名) 人物與機構之知識圖譜分析工具發展與數位人文應用 zh_TW dc.title (題名) Development and Application of the Knowledge Graph Analysis Tool of Characters and Institutions on Digital Humanities en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 一、中文文獻王昊奮、漆桂林、陳華鈞、潘志霖(2020)。人工智慧的神殿 : AI知識圖譜實作。台北市 : 深智。王鳳翔、王軍(2020)。基於知識圖譜的《論語》視覺化系統設計與建構。「第十一屆數位典藏與樹為人文國際研討會」發表之論文，中央研究院數位文化中心。李岡原(2005)。馬來西亞華人宗教探析。浙江師範大學學抱 : 社會科學版 第一期。邱偉雲(2011)。關鍵詞叢與文本意義挖掘的嘗試：以《清季外交史料》為例。載於項潔(主編)，數位人文在歷史學研究的應用，159-188。臺北市：國立臺灣大學出版中心。何啟良(2008)。馬來西亞華人人物志。拉曼大學中華研究中心。杜協昌(2018)。DocuSky 與文本字詞關聯圖的視覺化應用。「第九屆數位典藏與數位人文國際研討會」發表之論文，法鼓文理學院。金觀濤(2011)。數位人文研究的理論基礎。載於項潔(主編)，數位人文研究的新視野：基礎與想像，43-61。臺北市：國立臺灣大學出版中心。洪麗芬(2005)。馬來西亞華人和語言。八桂僑刊 第四期。陳志明，李遠龍(1998)。馬來西亞華人的認同。廣西民族大學學報: 哲學社會科學版 第四期。陳淑君、王祥安、沈漢聰(2020)。鏈結資料與知識圖譜在數位博物館的應用。「第十一屆數位典藏與樹為人文國際研討會」發表之論文，中央研究院數位文化中心。項潔、涂豐恩(2011)。導論――什麼是數位人文。載於項潔(主編)，從保存到創造: 開啟數位人文研究(9-28頁)。臺北市：國立臺灣大學出版中心。 二、英文文獻Al- Khatib, K., Yufang, H., Henning, W., Charles, J., Francesca, B., & Benno, S. 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