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標題: Automatically Categorizing Blog Articles Using Ontology Tree Built by DBpedia
作者: 簡學群
Hsueh-Chun Chien
關鍵字: Linked Data
Blog Connect
引用: '[1] J. Sobel, 'State of the Blogosphere 2010 Introduction,' 2010. [Online]. Available: [2] J. Sobel, 'State of the Blogosphere 2011: Introduction and Methodology,' 2011. [Online]. Available: [3] R. Mirizzi, A. Ragone, T. D. Noia and E. D. Sciascio, Ranking the Linked Data: the case of DBpedia, 2010. [4] T. Berners-Lee, 'Design Issues–Linked Data.,' 2007. [Online]. Available: [5] C. Bizer, T. Heath and T. Berners-Lee, 'Linked data - the story so far,' International Journal on Semantic Web and Information Systems, vol. 3, no. 5, pp. 1-22, 2009. [6] S. Auer, C. Bizer, G. Kobilarov, J. Lehmann, R. Cyganiak and Z. Ives, 'DBpedia: A Nucleus for a Web of Open Data,' in ISWC 2007 + ASWC 2007, Busan, Korea, 2007. [7] C. Bizer, J. Lehmann, G. Kobilarov, S. Auer, C. Becker, R. Cyganiak and S. Hellmann, 'DBpedia – A Crystallization Point for the Web of Data,' Journal of Web Semantics: Science, Services and Agents on the World Wide Web, pp. 154-165, 2009. [8] Y.-H. Chen, E.-L. Lu and T.-Y. 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Barkataki, 'Convergence of Web 2.0 and Semantic Web: A Semantic Tagging and Searching System for Creating and Searching Blogs,' in IEEE International Conference on Semantic Computing (ICSC), 2007. [14] G. Srinivas, N. Tandon and V. Varma, 'A weighted tag similarity measure based on a collaborative weight model,' in International Workshop on Search and Mining User-generated Contents, 2010. [15] Y. Zhang, K. GAO, B. Zhang, J. Guo, F. Gao and P. Guo, 'Clustering Blog Posts Using Tags and Relations in the Blogosphere,' in International Conference on Information Science and Engineering(ICISE), 2009. [16] C. Ouyang, X. Yang, X. Li and Z. Liu, 'Formal Concept Analysis Support for Web Document Clustering based on Social Tagging,' in International Conference on Uncertainty Reasoning and Knowledge Engineering (URKE), 2012. [17] M. N. Uddin, T. H. Duong, N. T. Nguyen, X. M. Q and G. S. Jo, 'Semantic similarity measures for enhancing information retrieval in folksonomies,' Expert Systems with Application, vol. 5, no. 40, pp. 1645-1653, 2013. [18] B. J. Jansen, D. L. Booth and A. Spink, 'Determining the User Intent of Web Search Engine Queries,' in International Conference on World Wide Web, 2007. [19] h. Becker and C. Bizer, 'DBpedia Mobile: A Location-Enabled Linked Data Browser,' in 1st International Workshop about Linked Data on the Web, 2008. [20] P. Mendes, M. Jakob, A. Garcia-Silva and C. Bizer, 'DBpedia Spotlight: Shedding Light on the Web of Documents,' in 7th International Conference on Semantic Systems, 2011. [21] J. Lehmann, J. Schuppel and S. Auer, 'Discovering Unknown Connections – the DBpedia Relationship Finder,' in 1st Conference on Social Semantic Web, 2007. [22] M. Morsey, J. Lehmann, S. Auer, C. Stadler and S. Hellmann, 'DBpedia and the live extraction of structured data from Wikipedia,' in 8th International Conference on Ontologies, DataBases, and Applications of Semantics (ODBASE), 2012. [23] L. Deligiannidis, K. J. Kochut and A. P. Sheth, 'RDF data exploration and visualization,' in CyberInfrastructure: information management in eScience, 2007. [24] P. Heim, S. Hellmann, J. Lehmann, S. Lohmann and T. Stegemann, 'RelFinder: Revealing Relationships in RDF Knowledge Base,' in 4th International Conference on Semantic and Digital Media Technologies: Semantic Multimedia, 2009. [25] C. Becker and C. Bizer, 'Exploring the Geospatial SemanticWeb with DBpedia Mobile,' Web Semantics: Science, Services and Agents on the World Wide Web, pp. 278-286, 2009. [26] Alias-i, 'LingPipe 4.0.0.,' 2008. [Online]. Available: [Accessed 24 8 2010]. [27] A. Passant, 'dbrec — Music Recommendations Using,' in 9th International Semantic Web Conference, ISWC 2010, 2010. [28] A. Bernstein, E. Kaufmann, C. Burki and M. 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摘要: Due to the rapid development of social networks, net users are only share information with friends in the closed social networks, but also share information with everyone on the net. However, blogs, one of the main platforms for social networks, are still like isolated island. To overcome this problem, we developed a platform called Blog Connect. With the developed scheme, we attempted to automatically create links among blog posts on Blog Connect and DBpedia which is one of the largest open datasets.
社群網路在近幾年來發展快速,網路使用者已經習慣由社群網路獲取資訊,從原來只限於親友的封閉社交圈,也慢慢的可以跟更多網友分享資訊。在部落格平台中,部落格作者通常會使用部落格平台所提供的分類來為文章設定類別,部落格讀者可以透過類別來找到其他相關的文章,但這種方式可能會因為部落格作者的錯誤或不了解類別的意思而指定錯誤的類別,造成使用者無法找到真正相關主題的文章。因此本研究試圖利用Linked Open Data(LOD)來為文章找出正確的類別。在本團隊過去的研究中,可以利用文章的查詢關鍵字來代表文章主題,因此我們延伸過去的研究成果,用查詢關鍵字來查詢DBpedia,整理每一篇文章的查詢結果可以得到其在DBpedia中的類別,並與人工分類做比較,驗證結果的正確率大於50%,代表結合LOD的資料足以找出文章的類別。
其他識別: U0005-0412201410222100
文章公開時間: 2015-12-05
Appears in Collections:資訊管理學系



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