Can social bookmarking enhance search in the web?

Social bookmarking is an emerging type of a Web service that helps users share, classify, and discover interesting resources. In this paper, we explore the concept of an enhanced search, in which data from social bookmarking systems is exploited for enhancing search in the Web. We propose combining the widely used link-based ranking metric with the one derived using social bookmarking data. First, this increases the precision of a standard link-based search by incorporating popularity estimates from aggregated data of bookmarking users. Second, it provides an opportunity for extending the search capabilities of existing search engines. Individual contributions of bookmarking users as well as the general statistics of their activities are used here for a new kind of a complex search where contextual, temporal or sentiment-related information is used. We investigate the usefulness of social bookmarking systems for the purpose of enhancing Web search through a series of experiments done on datasets obtained from social bookmarking systems. Next, we show the prototype system that implements the proposed approach and we present some preliminary results.

[1]  Bernardo A. Huberman,et al.  The Structure of Collaborative Tagging Systems , 2005, ArXiv.

[2]  Philip S. Yu,et al.  On the temporal dimension of search , 2004, WWW Alt. '04.

[3]  Junghoo Cho,et al.  Page quality: in search of an unbiased web ranking , 2005, SIGMOD '05.

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

[5]  D. Cuomo.  Onomi : Social Bookmarking on a Corporate Intranet , 2006 .

[6]  SaltonGerard,et al.  Term-weighting approaches in automatic text retrieval , 1988 .

[7]  Marc Najork,et al.  A large‐scale study of the evolution of Web pages , 2003, WWW '03.

[8]  Bernardo A. Huberman,et al.  Usage patterns of collaborative tagging systems , 2006, J. Inf. Sci..

[9]  Gerard Salton,et al.  Term-Weighting Approaches in Automatic Text Retrieval , 1988, Inf. Process. Manag..

[10]  Wei-Ying Ma,et al.  Probabilistic query expansion using query logs , 2002, WWW '02.

[11]  Mark Levene,et al.  Web Dynamics , 2004, Springer Berlin Heidelberg.

[12]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[13]  Ricardo A. Baeza-Yates,et al.  Web Dynamics, Structure, and Page Quality , 2004, Web Dynamics.

[14]  David Carmel,et al.  Trend detection through temporal link analysis , 2004, J. Assoc. Inf. Sci. Technol..

[15]  Ricardo A. Baeza-Yates,et al.  Query Recommendation Using Query Logs in Search Engines , 2004, EDBT Workshops.

[16]  Farshad Fotouhi,et al.  Emergent semantics and the multimedia semantic web , 2002, SGMD.

[17]  Daniel Gomes,et al.  Modelling information persistence on the web , 2006, ICWE '06.

[18]  Yong Yu,et al.  Emergent Semantics from Folksonomies: A Quantitative Study , 2006, J. Data Semant..

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

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

[21]  Thomas Mandl,et al.  Implementation and evaluation of a quality-based search engine , 2006, HYPERTEXT '06.

[22]  Peter Brusilovsky,et al.  Social navigation in web lectures , 2006, HYPERTEXT '06.

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

[24]  Taher H. Haveliwala.  Topic-Sensitive PageRank: A Context-Sensitive Ranking Algorithm for Web Search , 2003, IEEE Trans. Knowl. Data Eng..

[25]  David N. Sturtz,et al.  Communal Categorization: The Folksonomy , 2004 .

[26]  François Bry,et al.  Collaborative Categorization on the Web: Approach, Prototype, and Experience Report , 2003 .