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  1. NTU Theses and Dissertations Repository
  2. 電機資訊學院
  3. 資訊網路與多媒體研究所
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/44356
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dc.contributor.advisor鄭卜壬(Pu-Jen Cheng)
dc.contributor.authorHan-wen Changen
dc.contributor.author張瀚文zh_TW
dc.date.accessioned2021-06-15T02:52:53Z-
dc.date.available2009-08-04
dc.date.copyright2009-08-04
dc.date.issued2009
dc.date.submitted2009-08-04
dc.identifier.citation[1] Shenghua Bao , Guirong Xue , Xiaoyuan Wu , Yong Yu , Ben Fei , Zhong Su,
Optimizing web search using social annotations, Proceedings of the 16th international
conference on World Wide Web, May 08-12, 2007, Banff, Alberta, Canada
[2] Shengliang Xu , Shenghua Bao , Yunbo Cao , Yong Yu, Using social annotations to
improve language model for information retrieval, Proceedings of the sixteenth ACM
conference on Conference on information and knowledge management, November
06-10, 2007, Lisbon, Portugal
[3] Efficient Top-k Querying over Social-Tagging Networks(Ralf Schenkel, Tom Crecelius, Mouna Kacimi, Sebastian Michel, Thomas Neumann, Josiane X. Parreira, Gerhard Weikum,SIGIR08)
[4] Rosen-Zvi, M., Griffiths T., Steyvers, M., & Smyth, P. (2004). The Author-TopicModel for Authors and Documents. In 20th Conference on Uncertainty in Artificial Intelligence. Banff, Canada
[5] J. Tang, J. Zhang, L. Yao, J. Li, L. Zhang and Z. Su. ArnetMiner: Extraction and Mining of Academic Social Networks. In 14th Knowledge Discovery and Data Mining Conference (KDD), 2008
[6] P. S. Dodds, R. Muhamad, and D. J. Watts. An experimental study of search in global social networks. Science, 301:827-829, 2003.
[7] Information Retrieval using a Singular Value Decomposition Model of Latent Semantic Structure(G. W. Furnas, S. Deerwester, S. T. Dumais,T. K. Landauer, R. A. Harshman, L. A. Streeter, K. E. Lochbaum, Proceedings of the 11th annual international ACM SIGIR conference on Research and development in information retrieval)
[8] P. Heymann and H. Garcia-Molina. Collaborative creation of communal hierarchical taxonomies in social tagging systems.Technical Report 2006-10, Stanford University, April 2006.
[9] S. Sen et al. Tagging, communities, vocabulary, evolution. InCSCW, 2006.
[10] J. Zhu, Z. Nie, X. Liu, B. Zhang, and J. R. Wen. StatSnowball: a Statistical Approach to Extracting Entity Relationships. In 18th International World Wide Web Conference (WWW), 2009.
[11] A. Java, X. Song, and T. Finin, B. Tseng, Why We Twitter: Understanding Microblogging Usage and Communities. In 9th WEBKDD and 1st SNA-KDD Workshop, 2007.
[12] C. Tantipathananandh et al. A framework for community identification in dynamic social networks. In KDD, 2007.
[13] G. W. Flake, S. Lawrence, and C. L. Giles. Efficient identification of web communities. In Proceedings ofthe 6th International Conference on Knowledge
Discovery and Data Mining (ACM SIGKDD-2000),2000.
[14] D. Gibson, J. Kleinberg, and P. Raghavan. Inferring web communities from link topology. In Proceedings of the 9th ACM Conference on Hypertext and
Hypermedia, 1998.
[15] R. Kumar, P. Raghavan, S. Rajagopalan, and A. Tomkins. Trawling the web for emerging cybercommunities. In Proceedings of The 8th International World Wide Web Conference, 1999.
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/44356-
dc.description.abstractWeb 1.0與Web 2.0有兩項主要差異:
(一)•創作者個人資訊之揭露 (二)•標籤的出現與否
在Web 2.0 環境中搜尋資訊的方法與 Web 1.0 的不同,有其優點,但也有些問題:
(一)•只注重文件搜尋,而沒有排序人
(二)•若文件與人沒有和query相同的tag, 這些文件與人將不會被找出。
(三) •由人們透過tag叢集起來的社群,是主題社群,而非社會性的社群。
本論文嘗試解決上述中傳統 Web 2.0 環境中對於搜尋的問題。
我們提出一個新的 Web 2.0 搜尋架構,並實做一個在 Web 2.0 環境中搜尋及瀏覽的系統,此系統能達到:
(一)•文件擷取(二)•人物擷取(三)•主題之探勘(四)•社群之探勘(五)•同時標記主題與社群(六)使用者能在文件、人物、主題、社群、標籤之間做瀏覽。
本系統能輔助使用者快速透過超連結在 Web 2.0 環境中,針對上述五種物件進行探勘與搜尋,這種新型態的搜尋方式可以帶給使用者不同的觀點。
透過在 SIGIR、SIGGRAPH、SODA 蒐集到的資料集,在本系統架構上進行的實驗,
驗證了本系統可在多種資料集上進行文件及人物擷取,並能有效的增進使用者社群網路中對主題及社群的理解。
zh_TW
dc.description.abstractThe two main differences between web 1.0 and web 2.0 environments are:
(1) author space and (2) tagging .Hence, the ways of searching in web 1.0 or web 2.0 environments are not necessarily the same. In web 2.0 environments there are some limitation which the searches may pose:
(1). Previous Web 2.0 search focuses on documents search,
(2). documents and people will not be found if they don’t have the same tags to the query,and
(3). the communities clustered by the tags belong to topical community instead of social community.
In this paper we have proposed a framework to handle such restriction, and a system is thus built for web 2.0 searching and browsing. Our system has the abilities to do the followings in the social networks:
(1). Retrieving documents and people.(2). Mining topics and communities.(3). Labeling the topics and communities.(4). Enabling the users to browse among documents, people, topics, communities and tags.
Due to the mined information and hyperlinks through which the users can browse, the user's comprehension among the structures of the social are extended; the proposed system presents a way of surfing the social networks where the users may have never experienced before.
In the experiments we have evaluated the framework on the data sets crawled from SIGIR, SIGGRAPH, and SODA; we have found that our framework is effective in retrieving the documents and people from the social networks, and is able to improve the experiences and comprehension of the users when they browse through our system.
en
dc.description.provenanceMade available in DSpace on 2021-06-15T02:52:53Z (GMT). No. of bitstreams: 1
ntu-98-R96944022-1.pdf: 1516401 bytes, checksum: f1da0d848d5e5b07a05f5e5caff88acd (MD5)
Previous issue date: 2009
en
dc.description.tableofcontents摘要 i
Abstract ii
Acknowledgement iv
Table of Contents v
List of Figures vii
List of Tables viii
Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivations 3
1.3 Idea 5
1.4 Thesis Structure 7
Chapter 2 Related Work 8
2.1 Using Tags to Improve Search Performance 8
2.2 Community 9
Chapter 3 Proposed Approach 12
3.1 LSI Model 12
3.2 Documents Retrieval 17
3.3 People Retrieval 20
3.4 Topics Mining 23
3.5 Communities Mining 24
3.6 Labeling Topics& Communities 25
3.7 Discussion 27
Chapter 4 Experiment 29
4.1 Documents Retrieval 30
4.2 People Retrieval 39
4.3 Topics Mining 47
4.4 Communities Mining 51
Chapter 5 Conclusion 53
5.1 Summary of Contributions 53
5.2 Future Work 54
dc.language.isoen
dc.title在web2.0環境中,自動探勘主題與社群,及其在資料瀏覽上之應用zh_TW
dc.titleAutomatic Mining of Topics and Communities for Browsing in Web2.0en
dc.typeThesis
dc.date.schoolyear97-2
dc.description.degree碩士
dc.contributor.oralexamcommittee陳信希(HSIN-HSI CHEN),曾新穆(Shin-Mu Tseng),張嘉惠(Chia-Hui Chang)
dc.subject.keyword瀏覽,zh_TW
dc.subject.keywordweb2.0,browsing,en
dc.relation.page57
dc.rights.note有償授權
dc.date.accepted2009-08-04
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
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