Skip to main content
SpringerLink
Log in

Using Probabilistic Topic Models in Enterprise Social Software

  • Conference paper
Business Information Systems (BIS 2010)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 47))

Included in the following conference series:

Abstract

Enterprise social software (ESS) systems are open and flexible corporate environments which utilize Web 2.0 technologies to stimulate participation through informal interactions and aggregate these interactions into collective structures. A challenge in these systems is to discover, organize and manage the knowledge model of topics found within the enterprise. In this paper we aim to enhance the search and recommendation functionalities of ESS by extending their folksonomies and taxonomies with the addition of underlying topics through the use of probabilistic topic models. We employ Latent Dirichlet Allocation in order to elicit latent topics and use the latter to assess similarities in resource and tag recommendation as well as for the expansion of query results. As an application of our approach we extend the search and recommendation facilities of the Organik enterprise social system.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

We’re sorry, something doesn't seem to be working properly.

Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Reilly, T.: What is Web 2.0: Design patterns and business models for the next generation of software

    Google Scholar 

  2. McAfee, A.P.: Enterprise 2.0: The dawn of emergent collaboration. MIT Sloan Management Review 47, 21 (2006)

    Google Scholar 

  3. Eid, T.: The Enterprise Social Software Market Continues to Expand Gartner (2008)

    Google Scholar 

  4. Gilchrist, A.: Corporate taxonomies: report on a survey of current practice. Online Information Review 25, 94–103 (2001)

    Article  Google Scholar 

  5. Folksonomy Coinage and Definition, http://www.vanderwal.net/folksonomy.html

  6. Shirky, C., Butterfield, S., et al.: Folksonomy, or How I Learned to Stop Worrying and Love the Mess. (2005)

    Google Scholar 

  7. Dotsika, F.: Uniting formal and informal descriptive power: Reconciling ontologies with folksonomies. International Journal of Information Management 29, 407–415 (2009)

    Article  Google Scholar 

  8. Hofmann, T.: Probabilistic latent semantic indexing. In: Proceedings of the 22nd ACM SIGIR conference on Research and development in information retrieval, pp. 50–57 (1999)

    Google Scholar 

  9. Steyvers, M., Griffiths, T.: Probabilistic topic models. In: Handbook of Latent Semantic Analysis, pp. 424–440 (2007)

    Google Scholar 

  10. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. The Journal of Machine Learning Research 3, 993–1022 (2003)

    Article  MATH  Google Scholar 

  11. Steyvers, M., et al.: Probabilistic author-topic models for information discovery. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 306–315 (2004) doi:10.1145/1014052.1014087

    Google Scholar 

  12. Shilakes, C.C., Tylman, J.: Enterprise information portals. Merrill Lynch (November 16, 1998)

    Google Scholar 

  13. Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE transactions on knowledge and data engineering 17, 734–749 (2005)

    Article  Google Scholar 

  14. Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. Internet Computing, IEEE 7, 76–80 (2003)

    Article  Google Scholar 

  15. Schafer, J.B., Konstan, J., Riedi, J.: Recommender systems in e-commerce. In: Proceedings of the 1st ACM conference on Electronic commerce, pp. 158–166 (1999)

    Google Scholar 

  16. Jin, X., Zhou, Y., Mobasher, B.: Web usage mining based on probabilistic latent semantic analysis. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 197–205 (2004) doi:10.1145/1014052.1014076

    Google Scholar 

  17. Chen, W.Y., et al.: Collaborative filtering for orkut communities: discovery of user latent behavior. In: Proceedings of the 18th international conference on World wide web, pp. 681–690 (2009)

    Google Scholar 

  18. Haruechaiyasak, C., Damrongrat, C.: Article Recommendation Based on a Topic Model for Wikipedia Selection for Schools. Digital Libraries: Universal and Ubiquitous Access to Information, 339–342 (2008)

    Google Scholar 

  19. Schmitz, C., et al.: Mining Association Rules in Folksonomies. Data Science and Classification, 261-270 (2006), http://dx.doi.org/10.1007/3-540-34416-0_28

  20. Hotho, A., et al.: Information retrieval in folksonomies: Search and ranking

    Google Scholar 

  21. Krestel, R., Fankhauser, P., Nejdl, W.: Latent dirichlet allocation for tag recommendation. In: Proceedings of the third ACM conference on Recommender systems, pp. 61–68 (2009)

    Google Scholar 

  22. Voorhees, E.M.: Query expansion using lexical-semantic relations. In: Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 61–69 (1994), http://portal.acm.org/citation.cfm?id=188508

  23. Xu, J., Croft, W.B.: Query expansion using local and global document analysis. In: Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 4–11 (1996)

    Google Scholar 

  24. Park, L.A., Ramamohanarao, K.: The sensitivity of latent dirichlet allocation for information retrieval. In: Buntine, W., Grobelnik, M., Mladenić, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009. LNCS, vol. 5782, pp. 176–188. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  25. Uschold, M., Gruninger, M.: Ontologies and semantics for seamless connectivity. SIGMOD Rec. 33, 58–64 (2004)

    Article  Google Scholar 

  26. Lin, J.: Divergence measures based on the Shannon entropy. IEEE Transactions on Information theory 37, 145–151 (1991)

    Article  MATH  Google Scholar 

  27. Bibikas, D., et al.: A Sociotechnical Approach to Knowledge Management in the Era of Enterprise 2.0: The case of OrganiK. Scalable Computing: Practice and Experience 9, 315–327 (2008)

    Google Scholar 

  28. McCallum, A.K.: MALLET: A Machine Learning for Language Toolkit (2002), http://mallet.cs.umass.edu

  29. Passant, A., et al.: Semantic Search for Enterprise 2.0

    Google Scholar 

  30. Ehrlich, K., Lin, C., Griffiths-Fisher, V.: Searching for experts in the enterprise: combining text and social network analysis. In: Proceedings of the 2007 international ACM conference on Supporting group work, pp. 117–126 (2007)

    Google Scholar 

  31. Dmitriev, P.A., et al.: Using annotations in enterprise search. In: Proceedings of the 15th international conference on World Wide Web, pp. 811–817 (2006)

    Google Scholar 

  32. Zhen, L., Huang, G.Q., Jiang, Z.: An inner-enterprise knowledge recommender system. Expert Systems with Applications 37, 1703–1712 (2010)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. National Technical University of Athens, Iroon Polytechniou 9, Zografou Athens, 157 80, Greece

    Konstantinos Christidis & Gregoris Mentzas

Authors
  1. Konstantinos Christidis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gregoris Mentzas
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Information Systems, Poznań University of Economics, Al. Niepodległości 10, 61-875, Poznań, Poland

    Witold Abramowicz

  2. Institut für Informatik, Freie Universität Berlin, Takustr. 9, 14195, Berlin, Germany

    Robert Tolksdorf

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Christidis, K., Mentzas, G. (2010). Using Probabilistic Topic Models in Enterprise Social Software. In: Abramowicz, W., Tolksdorf, R. (eds) Business Information Systems. BIS 2010. Lecture Notes in Business Information Processing, vol 47. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12814-1_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-12814-1_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12813-4

  • Online ISBN: 978-3-642-12814-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation