Understanding the Semantics of Ambiguous Tags in Folksonomies

The use of tags to describe Web resources in a collaborative manner has experienced rising popularity among Web users in recent years. The product of such activity is given the name folksonomy, which can be considered as a scheme of organizing information in the users' own way. In this paper, we present a possible way to analyze the tripartite graphs - graphs involving users, tags and resources - of folksonomies and discuss how these elements acquire their meanings through their associations with other elements, a process we call mutual contextualization. In particular, we demonstrate how different meanings of ambiguous tags can be discovered through such analysis of the tripartite graph by studying the tag sf. We also discuss how the result can be used as a basis to better understand the nature of folksonomies.

[1]  Nigel Shadbolt,et al.  Tag Meaning Disambiguation through Analysis of Tripartite Structure of Folksonomies , 2007, 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops.

[2]  Thomas Gruber,et al.  Ontology of Folksonomy: A Mash-Up of Apples and Oranges , 2007, Int. J. Semantic Web Inf. Syst..

[3]  Adam Mathes,et al.  Folksonomies-Cooperative Classification and Communication Through Shared Metadata , 2004 .

[4]  Shinichi Honiden,et al.  Web Page Recommender System based on Folksonomy Mining for ITNG ’06 Submissions , 2006, Third International Conference on Information Technology: New Generations (ITNG'06).

[5]  Grigory Begelman,et al.  Automated Tag Clustering: Improving search and exploration in the tag space , 2006 .

[6]  Tony Hammond,et al.  Social Bookmarking Tools (I): A General Overview , 2005, D Lib Mag..

[7]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[8]  Satoru Kawai,et al.  An Algorithm for Drawing General Undirected Graphs , 1989, Inf. Process. Lett..

[9]  Peter Mika Ontologies Are Us: A Unified Model of Social Networks and Semantics , 2005, International Semantic Web Conference.

[10]  Harris Wu,et al.  Harvesting social knowledge from folksonomies , 2006, HYPERTEXT '06.

[11]  Vladimir Batagelj,et al.  Exploratory Social Network Analysis with Pajek , 2005 .

[12]  Mor Naaman,et al.  HT06, tagging paper, taxonomy, Flickr, academic article, to read , 2006, HYPERTEXT '06.

[13]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[14]  M. Newman Analysis of weighted networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[15]  Bethany S. Dohleman Exploratory social network analysis with Pajek , 2006 .

[16]  Andreas Hotho,et al.  Information Retrieval in Folksonomies: Search and Ranking , 2006, ESWC.

[17]  Yong Yu,et al.  Exploring social annotations for the semantic web , 2006, WWW '06.