Abstract
Social bookmarking systems have recently received an increasing attention in both academic and industrial communities. This success is owed to their ease of use that relies on a simple intuitive process, allowing their users to label diverse resources with freely chosen keywords aka tags. The obtained collections are known under the nickname of Folksonomy. In this paper, we introduce a new approach dedicated to the visualization of large folksonomies, based on the ”intersecting” minimal transversals. The main thrust of such an approach is the proposal of a reduced set of ”key” nodes of the folksonomy from which the remaining nodes would be faithfully retrieved. Thus, the user could navigate in the folksonomy through a folding/unfolding process.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Trabelsi, C., Jrad, A., Ben, S.: Yahia: Bridging folksonomies and domain ontologies: Getting out non-taxonomic relations. In: Proc. of the 2010 IEEE Intl. Conference on Data Mining Workshops, ICDMW 2010, pp. 369–379. IEEE Computer Society, Washington, DC (2010)
Lohmann, S., Díaz, P.: Representing and visualizing folksonomies as graphs: a reference model. In: Proc. of the Intl. Working Conference on Advanced Visual Interfaces, AVI 2012, pp. 729–732. ACM, New York (2012)
Scripps, J., Tan, P.N., Esfahanian, A.H.: Node roles and community structure in networks. In: Proc. of the 1st Workshop on Web Mining and Social Network Analysis (SNA-KDD 2007), San José, California, pp. 26–35 (2007)
Opsahl, T., Hogan, B.: Growth mechanisms in continuously-observed networks: Communication in a facebook-like community. CoRR (2010)
Jelassi, N., Largeron, C., Ben Yahia, S.: TMD-Miner: Une nouvelle approche pour la détection des diffuseurs dans un système communautaire. In: Actes de la 12eme Conférence Intl.e Francophone EGC, Bordeaux, France, pp. 423–428 (2012)
Shneiderman, B.: The eyes have it: A task by data type taxonomy for information visualization. In: Press, I.C.S. (ed.) Proc. IEEE Symposium on Visual Languages, Boulder, Colorado, pp. 336–343 (1996)
Hotho, A., Jäschke, R., Schmitz, C., Stumme, G.: Information retrieval in folksonomies: Search and ranking. In: Sure, Y., Domingue, J. (eds.) ESWC 2006. LNCS, vol. 4011, pp. 411–426. Springer, Heidelberg (2006)
Ganter, B., Wille, R.: Formal Concept Analysis. Springer, Heidelberg (1999)
Sen, S., Harper, M.F.M., Lapitz, A., Riedl, J.: The quest for quality tags. In: Proc. of the 2007 Intl. ACM Conference on Supporting Group Work, pp. 361–370. ACM (2007)
Krestel, R., Chen, L.: The art of tagging: Measuring the quality of tags. In: Domingue, J., Anutariya, C. (eds.) ASWC 2008. LNCS, vol. 5367, pp. 257–271. Springer, Heidelberg (2008)
Damme, C., Hepp, M., Coenen, T.: Quality Metrics for Tags of Broad Folksonomies. In: Proc. of I-semantics 2008, Graz, Austria (2008)
Gu, X., Wang, X., Li, R., Wen, K., Yang, Y., Xiao, W.: Measuring social tag confidence: is it a good or bad tag? In: Wang, H., Li, S., Oyama, S., Hu, X., Qian, T. (eds.) WAIM 2011. LNCS, vol. 6897, pp. 94–105. Springer, Heidelberg (2011)
Lohmann, S., Ziegler, J., Tetzlaff, L.: Comparison of tag cloud layouts: Task-related performance and visual exploration. In: Gross, T., Gulliksen, J., Kotzé, P., Oestreicher, L., Palanque, P., Prates, R.O., Winckler, M. (eds.) INTERACT 2009, Part I. LNCS, vol. 5726, pp. 392–404. Springer, Heidelberg (2009)
Kangpyo, L., Hyunwoo, K., Hyopil, S., Hyoung-Joo, K.: Folksoviz: A semantic relation-based folksonomy visualization using the wikipedia corpus. In: Proc. of the 10th Intl. Conference ACIS, pp. 24–29. IEEE Computer Society, Washington, DC (2009)
Lambiotte, R., Ausloos, M.: Collaborative tagging as a tripartite network. In: Alexandrov, V.N., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2006. LNCS, vol. 3993, pp. 1114–1117. Springer, Heidelberg (2006)
Dattolo, A., Pitassi, E.: Visualizing and managing folksonomies. In: Proc. of the Workshop on Semantic Adaptive Social Web, Girona, Spain, pp. 6–14. Springer (2011)
Ham, F.V., Perer, A.: “Search, show context, expand on demand”: Supporting large graph exploration with degree-of-interest. IEEE Trans. Vis. Comput. Graph. 15, 953–960 (2009)
Le Grand, B.: Extraction d’information et visualisation de systèmes complexes sémantiquement structurés. Doctorat d’université, Paris. Université Pierre et Marie Curie (Décembre 2001)
Trabelsi, C., Jelassi, N., Ben Yahia, S.: Scalable mining of frequent tri-concepts from folksonomies. In: Tan, P.-N., Chawla, S., Ho, C.K., Bailey, J. (eds.) PAKDD 2012, Part II. LNCS, vol. 7302, pp. 231–242. Springer, Heidelberg (2012)
Wasserman, S., Faust, K.: Social Network Analysis, methods and application (1994)
Scripps, J., Tan, P.N., Esfahanian, A.H.: Exploration of link structure and community-based node roles in network analysis. In: Proc. of the 7th IEEE Intl. Conference on Data Mining (ICDM 2007), pp. 649–654 (2007)
Forestier, M., Stavrianou, A., Velcin, J., Zighed, D.A.: Roles in social networks: Methodologies and research issues. Web Intelligence and Agent Systems 10, 117–133 (2012)
Borgatti, S.P., Everett, M.G.: Notions of Position in Social Network Analysis. Sociological Methodology 22, 1–35 (1992)
Anagnostopoulos, A., Kumar, R., Mahdian, M.: Influence and correlation in social networks. In: Proc. of the 14th ACM SIGKDD Intl. Conference, pp. 7–15. ACM, New York (2008)
Berge, C.: Hypergraphs: Combinatorics of finite sets, p. 256 (1989)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mouakher, A., Heymann, S., Yahia, S.B., Le Grand, B. (2013). Efficient Visualization of Folksonomies Based on «Intersectors ». In: Larsen, H.L., Martin-Bautista, M.J., Vila, M.A., Andreasen, T., Christiansen, H. (eds) Flexible Query Answering Systems. FQAS 2013. Lecture Notes in Computer Science(), vol 8132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40769-7_55
Download citation
DOI: https://doi.org/10.1007/978-3-642-40769-7_55
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40768-0
Online ISBN: 978-3-642-40769-7
eBook Packages: Computer ScienceComputer Science (R0)