Abstract
This paper is concerned with the problem of boosting social annotations using propagation, which is also called social propagation. In particular, we focus on propagating social annotations of web pages (e.g., annotations in Del.icio.us). Social annotations are novel resources and valuable in many web applications, including web search and browsing. Although they are developing fast, social annotations of web pages cover only a small proportion (<0.1%) of the World Wide Web. To alleviate the low coverage of annotations, a general propagation model based on Random Surfer is proposed. Specifically, four steps are included, namely basic propagation, multiple-annotation propagation, multiple-link-type propagation, and constraint-guided propagation. The model is evaluated on a dataset of 40,422 web pages randomly sampled from 100 most popular English sites and ten famous academic sites. Each page’s annotations are obtained by querying the history interface of Del.icio.us. Experimental results show that the proposed model is very effective in increasing the coverage of annotations while still preserving novel properties of social annotations. Applications of propagated annotations on web search and classification further verify the effectiveness of the model.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
Bao, S., Wu, X., Fei, B., Xue, G., Su, Z., Yu, Y.: Optimizing web search using social annotation. In: Proc. of WWW, pp. 501–510 (2007)
Borges, J., Levene, M.: Ranking pages by topology and popularity within web sites. World Wide Web J 9(3), 301–316 (2006). doi:10.1007/s11280-006-8558-y
Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. In: Computer Networks and ISDN Systems, 30(1–7):107–117 (1998)
Brooks, C.H., Montanez, N.: Improved annotation of the blogosphere via autotagging and hierarchical clustering. In: Proc. of WWW, pp. 583–589 (2006)
Chirita, P.-A., Costache, S., Handschuh, S., Nejdl, W.: P-TAG: Large scale automatic generation of personalized annotation TAGs for the web. In: WWW, pp. 845–854 (2007)
Crestani, F., Lee, P.L.: Searching the web by constrained spreading activation. In: Information Processing and Management. 36(4):585–605 (2000). July
Dmitriev, P.A., Eiron, N., Fontoura, M., Shekita, E.: Using annotations in enterprise search. In: Proc. of WWW, pp. 811–817 (2006)
Dubinko, M., Kumar, R., Magnani, J., Novak, J., Raghavan, P., Tomkins, A.: Visualizing tags over time. In: Proc. of WWW, pp. 193–202 (2006)
Flesca, S., Greco, S., Tagarelli, A., Zumpano, E.: Mining user preferences, page content and usage to personalize website navigation. World Wide Web J 8(3), 317–345 (2005). doi:10.1007/s11280-005-1315-9
Gao, X., Murugesan, S., Lo, B.W.N.: A simple method to extract key terms. Int J Electron Bus 4(3/4). IJEB. doi:10.1504/IJEB.2006.010863 (2006)
Golder, S.A., Huberman, B.A.: Usage patterns of collaborative tagging systems. J Inf Sci 32(2), 198–208 (2006). doi:10.1177/0165551506062337
Halpin, H., Robu, V., Shepherd, H.: The complex dynamics of collaborative tagging. In Proc. of WWW, pp. 211–220 (2007)
Henzinger, M.R., Heydon, A., Mitzenmacher, M., Najork, M.: Measuring index quality using random walks on the web. In: Proc. of WWW, pp. 213–225 (1999)
Hotho, A., Jaschke, R., Schmitz, C., Stumme, G.: Information retrieval in folksonomies: Search and ranking. In: Proc. of ESWC, pp.411–426 (2006)
Kamishima, T., Hamasaki, M., Akaho, S.: BaggTaming—Learning from wild and tame data. In: Proc. of ECML/PKDD2008 Workshop: Wiki, Blogs, Bookimarking Tools (2008)
Kleinberg, J.: Authoritative sources in a hyperlinked environment. In: Proc. of 9th Annual ACM-SIAM Symposium. Discrete Algorithms, pp. 668–677 (1998)
Kumar, R., Novak, J., Raghavan, P., Tomkins, A.: On the bursty evolution of blogspace. World Wide Web J 8(2), 159–178 (2005). doi:10.1007/s11280-004-4872-4
Li, R., Bao, S., Fei, B., Su, Z., Yu, Y.: Towards effective browsing of large scale social annotations. In: Proc. of WWW, pp. 943–952 (2007)
Mathes, A.: Folksonomies—Cooperative classification and communication through shared metadata. http://www.adammathes.com/academic/computer-mediated-communication/folksonomies.html, December (2004)
Mitchell, T.: Machine learning. McGraw-Hill (1997)
Merholz, P.: Metadata for the masses. October 19. http://www.adaptivepath.com/publications/essays/archives/000361.php (2004)
Mika, P.: Ontologies are us: A unified model of social networks and semantics. In: Proc. of ISWC, pp. 522–536 (2005)
Noll, M., Meinel, C.: Web search personalization via social bookmarking and tagging. In: Proc. of ISWC, pp. 367–380 (2007)
Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank citation ranking: Bringing order to the web. Technical report, Stanford Digital Library (1998)
Qin, T., Liu, T.-Y., Zhang, X.-D., Chen, Z., Ma, W.-Y.: A study of relevance propagation for web search. In: Proc. of SIGIR, pp. 408–415 (2005)
Quintarelli, E.: Folksonomies: Power to the people. Paper presented at the ISKO Italy-UniMIB meeting. http://www.iskoi.org/doc/folksonomies.htm (2005). June
Rattenbury, T., Good, N., Naaman, M.: Towards automatic extraction of event and place semantics from flickr tags. In: Proc. of SIGIR, pp. 103–110 (2007)
Robertson, S.E., Walker, S., Hancock-Beaulieu, M., Gull, A., Lau, M.: Okapi at TREC. In: Proc. of TREC, pp. 21–30 (1992)
Rumelhart, D., Norman, D.: Representation in memory. Technical Report, Department of Psychology and Institute of Cognitive Science, UCSD La Jolla, USA (1983)
Shakery, A., Zhai, C.: A probabilistic relevance propagation model for hypertext retrieval. In: Proc. of CIKM pp. 550–558 (2005)
Smith, G.: Atomiq: Folksonomy: social classification. http://atomiq.org/archives/2004/08/folksonomy_social_classification.html (2004). Aug 3
Wal, T.V.: Explaining and showing broad and narrow folksonomies. http://www.personalinfocloud.com/2005/02/ explaining_and_.html: (2005). February 21
Wang, J., Li, M., Li, Z., Ma, W.-y.: Learning ranking function via relevance propagation. Technical Report, Microsoft Research Asia (2005). November
Wu, X., Zhang, L., Yu, Y.: Exploring social annotations for the semantic web. In: Proc. of WWW, pp. 417–426 (2006)
Xu, S., Bao, S., Cao, Y., Yu, Y.: Using social annotations to improve language model for information retrieval. In: Proc. of CIKM, pp. 1003–1006 (2007)
Xue, G.-R., Yang, Q., Yu, Y., Zeng, H., Chen, Z.: Exploiting the hierarchical structure for web link analysis. In: Proc. SIGIR, pp. 186–193 (2005)
Yang, Y., Lie, X.: A re-examination of text categorization methods. In: Proc. of SIGIR, pp. 42–49 (1999)
Zhou, M., Bao, S., Wu, X., Yu, Y.: An unsupervised model for exploring hierarchical semantics from social annotations. In: Proc. of ISWC, pp.680–693 (2007)
Zhou, D., Weston, J., Gretton, A., Bousquet, O., Schölkopf, B.: Ranking on data manifolds. In: Proc. of NIPS (2003)
Zhou, D., Bian, J., Zheng, S., Zha, H., Giles, C.L.: Exploring social annotations for information retrieval. In: Proc. of WWW, pp. 715–724 (2008). [1]
Zhu, X., Ghahramani, Z.: Learning from labeled and unlabeled data with label propagation. Technical Report 02-107, CMU-CALD (2002)
Author information
Authors and Affiliations
Corresponding authors
Additional information
Categories and Subject DescriptorsH. [Information Systems]: Miscellaneous; H.3.1 [Information Storage and Retrieval]: Content Analysis and Indexing; I.2.6 [Artificial Intelligence]: Learning.
General Terms Algorithms, Experimentation, Human Factors.
Rights and permissions
About this article
Cite this article
Bao, S., Yang, B., Fei, B. et al. Social Propagation: Boosting Social Annotations for Web Mining. World Wide Web 12, 399–420 (2009). https://doi.org/10.1007/s11280-009-0068-2
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11280-009-0068-2