RichVSM: enRiched vector space models for folksonomies

ABSTRACT

People share millions of resources (photos, bookmarks, videos, etc.) in Folksonomies (like Flickr, Delicious, Youtube, etc.). To access and share resources, they add keywords called tags to the resources. As the tags are freely chosen keywords, it might not be possible for users to tag their resources with all the relevant tags. As a result, many resources lack sufficient number of relevant tags. The lack of relevant tags results into sparseness of data, and this sparseness of data makes many relevant resources unsearchable against user queries.

In this paper, we explore two dimensions of semantic relationships between tags, based on the context and the distribution of tags. We exploit semantic relationships between tags to reduce sparseness in Folksonomies and propose different enriched vector space models. We also propose a vector space model Best of Breed which utilizes appropriate enrichment method based on the type of the query. We evaluate the proposed methods on a large dataset of 27 million resources, 92 thousand tags and 94 million tag assignments. Experimental results show that the enriched vector space models help in improving search, especially for the rare queries which have few relevant resources in the sparse data.

REFERENCES

• 1: Rabeeh Abbasi , Sergey Chernov , Wolfgang Nejdl , Raluca Paiu , Steffen Staab, Exploiting Flickr Tags and Groups for Finding Landmark Photos, Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval, April 06-09, 2009, Toulouse, France [doi>10.1007/978-3-642-00958-7_62]
• 2: R. Abbasi and S. Staab. Introducing Triple Play for Improved Resource Retrieval in Collaborative Tagging Systems. In Proceedings of Exploiting Semantic Annotations in Information Retrieval (ESAIR), workshop at ECIR'08, March 2008.
• 3: R. Abbasi, S. Staab, and P. Cimiano. Organizing Resources on Tagging Systems using T-ORG. In Proceedings of Bridging the Gap between Semantic Web and Web 2.0, workshop at ESWC 2007, pages 97--110, 2007.
• 4: G. Begelman, P. Keller, and F. Smadja. Automated Tag Clustering: Improving search and exploration in the tag space. Proceedings of the Collaborative Web Tagging Workshop at WWW, 2006.
• 5: Ciro Cattuto , Dominik Benz , Andreas Hotho , Gerd Stumme, Semantic Grounding of Tag Relatedness in Social Bookmarking Systems, Proceedings of the 7th International Conference on The Semantic Web, October 26-30, 2008, Karlsruhe, Germany [doi>10.1007/978-3-540-88564-1_39]
• 6: S. Deerwester, S. Dumais, G. Furnas, T. Landauer, and R. Harshman. Indexing by Latent Semantic Analysis. Journal of the American Society for Information Science, 41(6):391--407, 1990.
• 7: J. Gonzalo, P. Clough, J. Karlgren, and U. SICS. Overview of iCLEF 2008: search log analysis for Multilingual Image Retrieval. In Cross Language Evaluation Forum (CLEF 2008) Workshop Notes, Aarhus (September 2008), 2008.
• 8: J. Gonzalo, J. Karlgren, and P. Clough. iCLEF 2006 Overview: Searching the Flickr WWW photo-sharing repository. Proceedings of Cross Language Evaluation Forum (CLEF), page 8, 2006.
• 9: Paul Heymann , Daniel Ramage , Hector Garcia-Molina, Social tag prediction, Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, July 20-24, 2008, Singapore, Singapore [doi>10.1145/1390334.1390425]
• 10: A. Hotho, R. Jaschke, C. Schmitz, and G. Stumme. Information Retrieval in Folksonomies: Search and Ranking. LECTURE NOTES IN COMPUTER SCIENCE, 4011:411, 2006.
• 11: Robert Jäschke , Leandro Marinho , Andreas Hotho , Lars Schmidt-Thieme , Gerd Stumme, Tag Recommendations in Folksonomies, Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases, September 17-21, 2007, Warsaw, Poland [doi>10.1007/978-3-540-74976-9_52]
• 12: Rui Li , Shenghua Bao , Yong Yu , Ben Fei , Zhong Su, Towards effective browsing of large scale social annotations, Proceedings of the 16th international conference on World Wide Web, May 08-12, 2007, Banff, Alberta, Canada [doi>10.1145/1242572.1242700]
• 13: Peter Mika, Ontologies are us: A unified model of social networks and semantics, Web Semantics: Science, Services and Agents on the World Wide Web, v.5 n.1, p.5-15, March, 2007 [doi>10.1016/j.websem.2006.11.002]
• 14: Greg Pass , Abdur Chowdhury , Cayley Torgeson, A picture of search, Proceedings of the 1st international conference on Scalable information systems, p.1-es, May 30-June 01, 2006, Hong Kong [doi>10.1145/1146847.1146848]
• 15: P. Schmitz. Inducing Ontology from Flickr Tags. In Proceedings of Collaborative WebTagging, workshop at WWW'06, May 2006.
• 16: Börkur Sigurbjörnsson , Roelof van Zwol, Flickr tag recommendation based on collective knowledge, Proceeding of the 17th international conference on World Wide Web, April 21-25, 2008, Beijing, China [doi>10.1145/1367497.1367542]
• 17: T. V. Wal. Explaining and showing broad and narrow folksonomies, 2005. Available at http://www.personalinfocloud.com/2005/02/ explaining_and_.html.
• 18: Leong Chee Wee , Samer Hassan, Exploiting Wikipedia for Directional Inferential Text Similarity, Proceedings of the Fifth International Conference on Information Technology: New Generations, p.686-691, April 07-09, 2008 [doi>10.1109/ITNG.2008.190]
• 19: Sihem Amer Yahia , Michael Benedikt , Laks V. S. Lakshmanan , Julia Stoyanovich, Efficient network aware search in collaborative tagging sites, Proceedings of the VLDB Endowment, v.1 n.1, August 2008 [doi>10.1145/1453856.1453934]
• 20: Ding Zhou , Jiang Bian , Shuyi Zheng , Hongyuan Zha , C. Lee Giles, Exploring social annotations for information retrieval, Proceeding of the 17th international conference on World Wide Web, April 21-25, 2008, Beijing, China [doi>10.1145/1367497.1367594]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Retrieval models

General Terms:
Algorithms, Experimentation, Performance

Keywords:
folksonomies, information retrieval, search, smoothing, sparseness reduction, tagging, vector space model, vector space models