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International Conference on Knowledge Science, Engineering and Management

KSEM 2011: Knowledge Science, Engineering and Management pp 183–195Cite as

Finding Experts in Tag Based Knowledge Sharing Communities

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 7091)

Abstract

With the rapid development of online Knowledge Sharing Communities (KSCs), the problem of finding experts becomes increasingly important for knowledge propagation and putting crowd wisdom to work. A recent development trend of KSCs is to allow users to add text tags for annotating their posts, which are more accurate than traditional category information. However, how to leverage these user-generated tags for finding experts is still under-explored. To this end, in this paper, we develop a novel approach for finding experts in tag based KSCs by leveraging tag context and the semantic relationship between tags. Specifically, the extracted prior knowledge and user profiles are first used for enriching the query tags to infer tag context, which represents the user’s latent information needs. Then, a topic model based approach is applied for capturing the semantic relationship between tags and then taking advantage of them for ranking user authority. We evaluate the proposed framework for expert finding on a large-scale real-world data set collected from a tag based Chinese commercial Q&A web site. Experimental results clearly show that the proposed method outperforms several baseline methods with a significant margin.

Keywords

  • Expert finding
  • knowledge sharing communities
  • question answering
  • user-generated tags
  • topic models

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  • DOI: 10.1007/978-3-642-25975-3_17
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References

  1. http://wenda.google.com.hk/

  2. http://wenda.tianya.cn/

  3. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: VLDB 1994 (1994)

    Google Scholar 

  4. Azzopardi, L., Girolami, M., Risjbergen, K.V.: Investigating the relationship between language model perplexity and ir precision-recall measures. In: SIGIR 2003 (2003)

    Google Scholar 

  5. Balog, K., Azzopardi, L., Rijke, M.D.: Formal models for expert finding in enterprise corpora. Research and Development in Information Retrieval

    Google Scholar 

  6. Bao, T., Cao, H., Chen, E., Tian, J., Xiong, H.: An unsupervised approach to modeling personalized contexts of mobile users. In: ICDM 2010 (2010)

    Google Scholar 

  7. Blei, D.M., Ng, A.Y., Jordan, M.I.: Lantent dirichlet allocation. Journal of Machine Learning Research

    Google Scholar 

  8. Bouguessa, M., Dumoulin, B., Wang, S.: Identifying authoritative actors in question-answering forums: the case of yahoo! answers. In: KDD 2008 (2008)

    Google Scholar 

  9. Cong, G., Wang, L., Lin, C.-Y., Song, Y.-I., Sun, Y.: Finding question-answer pairs from online forums. In: SIGIR 2008 (2008)

    Google Scholar 

  10. Feng, D., Shaw, E., Hovy, E.: Mining and assessing discussions on the web through speech act analysis. In: ISWC 2006 Workshop on WCMHLT (2006)

    Google Scholar 

  11. Griffiths, T.L., Steyvers, M.: Finding scientific topics. Proceedings of National Academy of Science of the USA

    Google Scholar 

  12. Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. SIGMOD Rec.

    Google Scholar 

  13. Heinrich, G.: Parameter estimation for text analysis. Technical report, University of Lipzig

    Google Scholar 

  14. Hofmann, T.: Probabilistic latent semantic indexing. In: SIGIR 1999 (1999)

    Google Scholar 

  15. Jurczyk, P., Agichtein, E.: Discovering authorities in question answer communities by using link analysis. In: CIKM 2007 (2007)

    Google Scholar 

  16. Kao, W.-C., Liu, D.-R., Wang, S.-W.: Expert finding in question-answering websites: a novel hybrid approach. In: SAC 2010 (2010)

    Google Scholar 

  17. Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. Journal of the ACM

    Google Scholar 

  18. Liu, X., Croft, W.B., Koll, M.: Finding experts in community-based question-answering services. In: CIKM 2005 (2005)

    Google Scholar 

  19. Lu, Y., Quan, X., Ni, X., Liu, W., Xu, Y.: Latent link analysis for expert finding in user-interactive question answering services. In: SKG 2009 (2009)

    Google Scholar 

  20. Nigam, K., McCallum, A.K., Thrun, S., Mitchell, T.: Text classification from labeled and unlabeled documents using em. In: Machine Learning

    Google Scholar 

  21. Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: Bringing order to the web. Stanford Digital Library Technical Report

    Google Scholar 

  22. Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U., Chun Hsu, M.: Prefixspan: Mining sequential patterns efficiently by prefix-projected pattern growth. In: ICDE 2001 (2001)

    Google Scholar 

  23. Yeh, T., Darrell, T.: Multimodal question answering for mobile devices. In: IUI 2008 (2008)

    Google Scholar 

  24. Zhang, J., Ackerman, M.S., Adamic, L.: Expertise networks in online communities: structure and algorithms. In: WWW 2007 (2007)

    Google Scholar 

  25. Zhang, J., Tang, J., Li, J.: Expert Finding in a Social Network. In: Kotagiri, R., Radha Krishna, P., Mohania, M., Nantajeewarawat, E. (eds.) DASFAA 2007. LNCS, vol. 4443, pp. 1066–1069. Springer, Heidelberg (2007)

    CrossRef  Google Scholar 

  26. Zhang, J., Tang, J., Liu, L., Li, J.: A Mixture Model for Expert Finding. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 466–478. Springer, Heidelberg (2008)

    CrossRef  Google Scholar 

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Author information

Authors and Affiliations

  1. University of Science and Technology of China, China

    Hengshu Zhu & Enhong Chen

  2. Nokia Research Center, China

    Hengshu Zhu & Huanhuan Cao

Authors
  1. Hengshu Zhu
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  2. Enhong Chen
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  3. Huanhuan Cao
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Editor information

Editors and Affiliations

  1. Management Science and Information Systems Department, Rutgers, the State University of New Jersey, 1, Washington Park, 07102, Newark, NJ, USA

    Hui Xiong

  2. Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China

    W. B. Lee

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Zhu, H., Chen, E., Cao, H. (2011). Finding Experts in Tag Based Knowledge Sharing Communities. In: Xiong, H., Lee, W.B. (eds) Knowledge Science, Engineering and Management. KSEM 2011. Lecture Notes in Computer Science(), vol 7091. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25975-3_17

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  • DOI: https://doi.org/10.1007/978-3-642-25975-3_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25974-6

  • Online ISBN: 978-3-642-25975-3

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