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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 陳炳宇(Robin Bing-yu Chen) | |
dc.contributor.author | Ting-Yen Lee | en |
dc.contributor.author | 李庭嫣 | zh_TW |
dc.date.accessioned | 2021-05-20T21:29:34Z | - |
dc.date.available | 2010-08-20 | |
dc.date.available | 2021-05-20T21:29:34Z | - |
dc.date.copyright | 2010-08-20 | |
dc.date.issued | 2010 | |
dc.date.submitted | 2010-08-18 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/10438 | - |
dc.description.abstract | 隨著Web2.0的普及,網際網路上充滿各式各樣的知識。然而因為這麼龐大的知識庫的組織卻是零散的,造成了人類學習新知的困擾。因此,我們希望藉由自動化的提供各種知識的發展流程,當人類需要學習新知時,可以藉由這些新知的來源與原來自己的知識互相比對,進而了解相關領域的結構,幫助人類快速進入狀況。
在本論文中,我們聚焦於大多數學生最容易遇到的學術論文的知識發展趨勢。我們先將論文所描述的主題用關鍵字代表,結合論文間會相互引述的關係,可以將相關的主題整合在一起,並且找到這些相關主題之間發展的關係,如:合併、分裂等。 藉由提供知識演化趨勢的發展,人們可以在踏入新的領域前,先對此領域有個基礎的認知,幫助人類的學習。 我們也收集了32,044篇學術論文來驗證,結果顯示我們的方法可以有效的將相關主題整合,並且提供知識的演化發展。 | zh_TW |
dc.description.abstract | The World Wide Web is a rich source of knowledge. As many documents on the web are unstructured, it is difficult for people to learn efficiently in a systematic fashion. To facilitate learning, we aim to organize web resources according to the evolution of topics, which is a common organization used in many textbooks. In this research, we propose an approach to mining knowledge evolution from an academic archive by taking advantage of the citations between the literatures. Relationships among keywords are first extracted from each paper, and a citation graph is constructed from the references among papers to capture their dependency over time. Similar keywords can then be clustered into aggregated topics to track the evolution of knowledge. Finally, we propose methods to visualize knowledge evaluation by viewing both the evolution and interaction of aggregated topics. The experiments are conducted on 32,044 papers crawled from the ACM Digital Library, and the results show that the proposed approach is effective in mining knowledge evolution to help users learn from online resources. | en |
dc.description.provenance | Made available in DSpace on 2021-05-20T21:29:34Z (GMT). No. of bitstreams: 1 ntu-99-R97944018-1.pdf: 2932996 bytes, checksum: 974653677e3d02618f903cc13e2c8c65 (MD5) Previous issue date: 2010 | en |
dc.description.tableofcontents | Chapter 1 Introduction 1
1.1 Background ................................ 1 1.2 Motivation ................................. 2 1.3 Research Objectives ............................ 2 1.4 Thesis Organization ............................ 3 Chapter 2 Related Work 4 2.1 Topic Evolution .............................. 4 2.2 Visualizing the Evolution ......................... 6 Chapter 3 Topic Evolution 9 3.1 Problem Definition ............................ 9 3.1.1 Data Input ............................ 10 3.1.2 Data Output ........................... 11 3.2 Proposed Solution ............................. 12 3.3 Graph Modal ............................... 13 3.3.1 Paper Citation Graph ...................... 13 3.3.2 Mediator Graph .......................... 14 3.3.3 Topic Evolution Graph ...................... 16 Chapter 4 The Evolution Graph 17 4.1 Extract keyword Relation from the Citation Graph .......... 17 4.1.1 Co-Occurrence .......................... 19 4.1.2 Mutual Link ............................ 20 4.1.3 Co-Reference ........................... 21 4.1.4 Co-Citation ............................ 21 4.1.5 Combination of the keyword relation .............. 22 4.2 Aggregation With Time Effects ..................... 22 4.2.1 Group Keywords into Topics ................... 23 4.2.2 Aggregate Topics ......................... 24 4.3 Construct the Aggregated Topics Evolution Graph ........... 25 Chapter 5 Agent Approach 27 5.1 System Architecture ........................... 27 5.2 Topic Agent ................................ 28 5.3 Aggregated Topic Agent ......................... 30 Chapter 6 Visualization of the Evolution 32 6.1 Visualize Evolution of Aggregated Topics ................ 34 6.1.1 Layout ............................... 34 6.1.2 Representation of An Aggregated Topics ............ 35 6.1.3 Color ................................ 36 6.2 Visualize Interaction of Aggregated Topics ............... 37 6.2.1 Layout ............................... 38 Chapter 7 Experimental Results 41 7.1 Keyword Citation Graph and Keyword Relation Graph ........ 42 7.2 Topic Relation Graph ........................... 43 7.2.1 Topic-Year Distribution ..................... 47 7.2.2 Topic-Keyword Distribution ................... 48 7.2.3 Case Study ............................ 52 7.3 Aggregated Topic ............................. 54 7.3.1 Topic-Aggregated Topic Distribution .............. 56 7.3.2 keyword-Aggregated Topic Distribution ............. 56 7.3.3 Aggregated Topic Over Time .................. 59 7.4 Evolution ................................. 65 Chapter 8 Conclusion 68 8.1 Summary of Contributions ........................ 69 8.2 Future Work ................................ 70 Bibliography 72 | |
dc.language.iso | en | |
dc.title | 學術論文之知識演化趨勢及其可視化研究 | zh_TW |
dc.title | Mining and Visualization of Knowledge Evolution From An Academic Archive | en |
dc.type | Thesis | |
dc.date.schoolyear | 98-2 | |
dc.description.degree | 碩士 | |
dc.contributor.coadvisor | 許永真(Jane Yung-jen Hsu) | |
dc.contributor.oralexamcommittee | 林守德(Shou-De Lin),林光龍(Kuang-Lung Lin),楊傳凱(Chuan-kai Yang) | |
dc.subject.keyword | 學術論文,知識演化,趨勢,可視化研究, | zh_TW |
dc.subject.keyword | Mining,Visualization,Knowledge Evolution,Academic Archive, | en |
dc.relation.page | 76 | |
dc.rights.note | 同意授權(全球公開) | |
dc.date.accepted | 2010-08-19 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊網路與多媒體研究所 | zh_TW |
顯示於系所單位: | 資訊網路與多媒體研究所 |
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