Exploring social annotations for information retrieval

ABSTRACT

Social annotation has gained increasing popularity in many Web-based applications, leading to an emerging research area in text analysis and information retrieval. This paper is concerned with developing probabilistic models and computational algorithms for social annotations. We propose a unified framework to combine the modeling of social annotations with the language modeling-based methods for information retrieval. The proposed approach consists of two steps: (1) discovering topics in the contents and annotations of documents while categorizing the users by domains; and (2) enhancing document and query language models by incorporating user domain interests as well as topical background models. In particular, we propose a new general generative model for social annotations, which is then simplified to a computationally tractable hierarchical Bayesian network. Then we apply smoothing techniques in a risk minimization framework to incorporate the topical information to language models. Experiments are carried out on a real-world annotation data set sampled from del.icio.us. Our results demonstrate significant improvements over the traditional approaches.

References

1. T. Berners-Lee, J. Hendler, and O. Lassila. The semantic web. Scientific American, 284(5):34--43, 2001.
2. D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. Journal of Machine Learning Research, 2003.
3. S. Dill, N. Eiron, D. Gibson, D. Gruhl, R. Guha, A. Jhingran, T. Kanungo, S. Rajagopalan, A. Tomkins, J. A. Tomlin, and J. Y. Zien. Semtag and seeker: bootstrapping the semantic web via automated semantic annotation. In Proceedings of the 12th international conference on World Wide Web, pages 178--186, 2003.
4. S. Golder and B. A. Huberman. Usage patterns of collaborative tagging systems. Journal of Information Science, pages 198--208, 2006.
5. T. Griffiths and M. Steyvers. Finding scientific topics. In National Academy of Sciences, 2004.
6. A. Hotho, R. Jaschke, C. Schmitz, and G. Stumme. Information retrieval in folksonomies: Search and ranking. In Y. Sure and J. Domingue, editors, The Semantic Web: Research and Applications, volume 4011 of LNAI, pages 411--426, Heidelberg, June 2006. Springer.
7. P. Jackson. Introduction to expert systems. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 1986.
8. K. Jarvelin and J. Kekalainen. IR evaluation methods for retrieving highly relevant documents. In Proceedings of the 23rd annual international ACM SIGIR conference on research and development in information retrieval, pages 41--48, 2000.
9. F. Jelinek and R. Mercer. Interpolated estimation of markov source parameters from sparse data. In Pattern recognition in Practice, 1980.
10. R. Kumar, J. Novak, P. Raghavan, and A. Tomkins. On the bursty evolution of blogspace. In Proceedings of the 12th international conference on World Wide Web, pages 568--576, 2003.
11. O. Kurland, L. Lee, and C. Domshlak. Better than the real thing?: iterative pseudo-query processing using cluster-based language models. In SIGIR ?05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, pages 19--26, New York, NY, USA, 2005. ACM Press.
12. J. Lafferty and C. Zhai. Document language models, query models, and risk minimization for information retrieval. In SIGIR ?01: Proceedings of the 24th annual international conference on Research and development in information retrieval, pages 111--119, 2001.
13. A. K. McCallum. Multi-label text classification with a mixture model trained by em. In AAAI?09 Workshop on Text Learning, 1999.
14. Q. Mei and C. Zhai. Discovering evolutionary theme patterns from text: an exploration of temporal text mining. In KDD ?05: Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, pages 198--207, New York, NY, USA, 2005. ACM Press.
15. J. M. Ponte and W. B. Croft. A language modeling approach to information retrieval. In SIGIR ?98: Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval, pages 275--281, New York, NY, USA, 1998. ACM Press.
16. C. P. Robert and G. Casella. Monte Carlo Statistical Methods. Springer Publisher, 2nd Edition, 2005.
17. M. Rosen-Zvi, T. Griffiths, M. Steyvers, and P. Smyth. The author-topic model for authors and documents. In UAI ?04: Proceedings of the 20th conference on Uncertainty in artificial intelligence, pages 487--494. UAI Press, 2004.
18. M. Steyvers, P. Smyth, M. Rosen-Zvi, and T. Griffiths. Probabilistic author-topic models for information discovery. In KDD ?04: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 306--315. ACM Press, 2004.
19. T. Tao, X. Wang, Q. Mei, and C. Zhai. Language model information retrieval with document expansion. In HLT-NAACL, 2006.
20. X. Wu, L. Zhang, and Y. Yu. Exploring social annotations for the semantic web. In WWW ?06: Proceedings of the 15th international conference on World Wide Web, pages 417--426, New York, NY, USA, 2006. ACM Press.
21. C. Zhai and J. Lafferty. A study of smoothing methods for language models applied to information retrieval. ACM Transaction of information System, 22(2):179--214, 2004.
22. D. Zhou, E. Manavoglu, J. Li, C. L. Giles, and H. Zha. Probabilistic models for discovering e-communities. In WWW ?06: Proceedings of the 15th international conference on World Wide Web, pages 173--182. ACM Press, 2006.

Index Terms

• Information systems

  • Information retrieval

    • Retrieval tasks and goals

      • Clustering and classification

  • Information systems applications

    • Data mining

      • Clustering

• Mathematics of computing

  • Probability and statistics