skip to main content
10.1145/2481492.2481508acmconferencesArticle/Chapter ViewAbstractPublication PageshtConference Proceedingsconference-collections
research-article

Graph based techniques for tag cloud generation

Published:01 May 2013Publication History

ABSTRACT

Tag cloud is one of the navigation aids for exploring documents. Tag cloud also link documents through the user defined terms. We explore various graph based techniques to improve the tag cloud generation. Moreover, we introduce relevance measures based on underlying data such as ratings or citation counts for improved measurement of relevance of tag clouds. We show, that on the given data sets, our approach outperforms the state of the art baseline methods with respect to such relevance by 41 % on Movielens dataset and by 11 % on Bibsonomy data set.

References

  1. H. Aras, S. Siegel, and R. Malaka. Semantic cloud: an enhanced browsing interface for exploring resources in folksonomy systems. Workshop on Visual Interfaces to the Social and Semantic Web (VISSW2010), IUI2010, Feb7, 2010, Hong Kong, China, 2009.Google ScholarGoogle Scholar
  2. G. Begelman, P. Keller, and F. Smadja. Automated tag clustering: Improving search and exploration in the tag space. In Collaborative Web Tagging Workshop at WWW2006, Edinburgh, Scotland, pages 15--33. Citeseer, 2006.Google ScholarGoogle Scholar
  3. A. Borodin, G. Roberts, J. Rosenthal, and P. Tsaparas. Finding authorities and hubs from link structures on the world wide web. In Proceedings of the 10th international conference on World Wide Web, pages 415--429. ACM, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. U. Brandes. On variants of shortest-path betweenness centrality and their generic computation. Social Networks, 30(2):136--145, 2008.Google ScholarGoogle ScholarCross RefCross Ref
  5. H. Chang, D. Cohn, A. Mccallum, et al. Learning to create customized authority lists. In MACHINE LEARNING-INTERNATIONAL WORKSHOP THEN CONFERENCE-, pages 127--134, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. M. Grahl, A. Hotho, and G. Stumme. Conceptual clustering of social bookmarking sites. In Proceedings of I-KNOW, volume 7, pages 5--7, 2007.Google ScholarGoogle Scholar
  7. T. Haveliwala. Topic-sensitive pagerank: A context-sensitive ranking algorithm for web search. Knowledge and Data Engineering, IEEE Transactions on, 15(4):784--796, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. T. Haveliwala, S. Kamvar, and G. Jeh. An analytical comparison of approaches to personalizing pagerank. 2003.Google ScholarGoogle Scholar
  9. A. Hotho, R. Jäschke, C. Schmitz, and G. Stumme. Folkrank: A ranking algorithm for folksonomies. Proc. FGIR, 2006, 2006.Google ScholarGoogle Scholar
  10. J. Kleinberg, R. Kumar, P. Raghavan, S. Rajagopalan, and A. Tomkins. The web as a graph: Measurements, models, and methods. Computing and Combinatorics, pages 1--17, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Knowledge and U. o. K. Data Engineering Group. Benchmark folksonomy data from bibsonomy, version of january 1st, 2010.Google ScholarGoogle Scholar
  12. M. Leginus, P. Dolog, R. Lage, and F. A. Durão. Methodologies for improved tag cloud generation with clustering. In M. Brambilla, T. Tokuda, and R. Tolksdorf, editors, Web Engineering - 12th International Conference, ICWE 2012, volume 7387 of Lecture Notes in Computer Science, pages 61--75, Berlin, Germany, July 2012. Springer. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. A. Mathes. Folksonomies-cooperative classification and communication through shared metadata. Computer Mediated Communication, 47(10), 2004.Google ScholarGoogle Scholar
  14. P. Mika. Ontologies are us: A unified model of social networks and semantics. The Semantic Web--ISWC 2005, pages 522--536, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. L. Page, S. Brin, R. Motwani, and T. Winograd. The pagerank citation ranking: bringing order to the web. 1999.Google ScholarGoogle Scholar
  16. A. W. Rivadeneira, D. M. Gruen, M. J. Muller, and D. R. Millen. Getting our head in the clouds: toward evaluation studies of tagclouds. In Proceedings of the SIGCHI conference on Human factors in computing systems, CHI '07, pages 995--998, New York, NY, USA, 2007. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. J. Sinclair and M. Cardew-Hall. The folksonomy tag cloud: when is it useful? Journal of Information Science, 34(1):15--29, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. L. Specia and E. Motta. Integrating folksonomies with the semantic web. The semantic web: research and applications, pages 624--639, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. P. Venetis, G. Koutrika, and H. Garcia-Molina. On the selection of tags for tag clouds. In Proceedings of the fourth ACM international conference on Web search and data mining, WSDM '11, pages 835--844, New York, NY, USA, 2011. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. S. White and P. Smyth. Algorithms for estimating relative importance in networks. In Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '03, pages 266--275, New York, NY, USA, 2003. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. G. Xu, Y. Zong, R. Pan, P. Dolog, and P. Jin. On kernel information propagation for tag clustering in social annotation systems. In Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems -Volume Part II, KES'11, pages 505--514, Berlin, Heidelberg, 2011. Springer-Verlag. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Graph based techniques for tag cloud generation

              Recommendations

              Comments

              Login options

              Check if you have access through your login credentials or your institution to get full access on this article.

              Sign in
              • Published in

                cover image ACM Conferences
                HT '13: Proceedings of the 24th ACM Conference on Hypertext and Social Media
                May 2013
                275 pages
                ISBN:9781450319676
                DOI:10.1145/2481492

                Copyright © 2013 ACM

                Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

                Publisher

                Association for Computing Machinery

                New York, NY, United States

                Publication History

                • Published: 1 May 2013

                Permissions

                Request permissions about this article.

                Request Permissions

                Check for updates

                Qualifiers

                • research-article

                Acceptance Rates

                HT '13 Paper Acceptance Rate16of96submissions,17%Overall Acceptance Rate378of1,158submissions,33%

                Upcoming Conference

                HT '24
                35th ACM Conference on Hypertext and Social Media
                September 10 - 13, 2024
                Poznan , Poland

              PDF Format

              View or Download as a PDF file.

              PDF

              eReader

              View online with eReader.

              eReader