Extracting Semantic User Networks from Informal Communication Exchanges

  • Anna Lisa Gentile
  • Vitaveska Lanfranchi
  • Suvodeep Mazumdar
  • Fabio Ciravegna
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7031)

Abstract

Nowadays communication exchanges are an integral and time consuming part of people’s job, especially for the so called knowledge workers. Contents discussed during meetings, instant messaging exchanges, email exchanges therefore constitute a potential source of knowledge within an organisation, which is only shared with those immediately involved in the particular communication act. This poses a knowledge management issue, as this kind of contents become “buried knowledge”. This work uses semantic technologies to extract buried knowledge, enabling expertise finding and topic trends spotting. Specifically we claim it is possible to automatically model people’s expertise by monitoring informal communication exchanges (email) and semantically annotating their content to derive dynamic user profiles. Profiles are then used to calculate similarity between people and plot semantic knowledge-based networks. The major contribution and novelty of this work is the exploitation of semantic concepts captured from informal content to build a semantic network which reflects people expertise rather than capturing social interactions. We validate the approach using contents from a research group internal mailing list, using email exchanges within the group collected over a ten months period.

References

  1. 1.
    Abel, F., Gao, Q., Houben, G.J., Tao, K.: Analyzing User Modeling on Twitter for Personalized News Recommendations. In: Konstan, J., Conejo, R., Marzo, J., Oliver, N. (eds.) UMAP 2011. LNCS, vol. 6787, pp. 1–12. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  2. 2.
    Abel, F., Gao, Q., Houben, G.J., Tao, K.: Semantic Enrichment of Twitter Posts for User Profile Construction on the Social Web. In: Antoniou, G., Grobelnik, M., Simperl, E.P.B., Parsia, B., Plexousakis, D., Leenheer, P.D., Pan, J.Z. (eds.) ESWC 2011, Part II. LNCS, vol. 6644, pp. 375–389. Springer, Heidelberg (2011)Google Scholar
  3. 3.
    Adamic, L., Adar, E.: How to search a social network. Social Networks 27(3), 187–203 (2005)CrossRefGoogle Scholar
  4. 4.
    Balabanović, M., Shoham, Y.: Fab: content-based, collaborative recommendation. Commun. ACM 40, 66–72 (1997)CrossRefGoogle Scholar
  5. 5.
    Campbell, C.S., Maglio, P.P., Cozzi, A., Dom, B.: Expertise identification using email communications. In: Proceedings of the Twelfth International Conference on Information and Knowledge Management, CIKM 2003, pp. 528–531. ACM, New York (2003)CrossRefGoogle Scholar
  6. 6.
    Cortes, C., Pregibon, D., Volinsky, C.: Computational methods for dynamic graphs. Journal Of Computational And Graphical Statistics 12, 950–970 (2003)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Culotta, A., Bekkerman, R., McCallum, A.: Extracting social networks and contact information from email and the web. In: CEAS 2004: Proc. 1st Conference on Email and Anti-Spam (2004)Google Scholar
  8. 8.
    Daoud, M., Tamine, L., Boughanem, M.: A Personalized Graph-Based Document Ranking Model Using a Semantic User Profile. In: De Bra, P., Kobsa, A., Chin, D. (eds.) UMAP 2010. LNCS, vol. 6075, pp. 171–182. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    De Choudhury, M., Mason, W.A., Hofman, J.M., Watts, D.J.: Inferring relevant social networks from interpersonal communication. In: Proceedings of the 19th International Conference on World Wide Web, WWW 2010, pp. 301–310. ACM, New York (2010)Google Scholar
  10. 10.
    Diesner, J., Frantz, T.L., Carley, K.M.: Communication networks from the enron email corpus ”it’s always about the people. enron is no different”. Comput. Math. Organ. Theory 11, 201–228 (2005)CrossRefMATHGoogle Scholar
  11. 11.
    Eckmann, J., Moses, E., Sergi, D.: Entropy of dialogues creates coherent structures in e-mail traffic. Proceedings of the National Academy of Sciences of the United States of America 101(40), 14333–14337 (2004)MathSciNetCrossRefMATHGoogle Scholar
  12. 12.
    Freeman, L.C.: Visualizing Social Networks. JoSS: Journal of Social Structure 1(1) (2000)Google Scholar
  13. 13.
    Gentile, A.L., Basave, A.E.C., Dadzie, A.S., Lanfranchi, V., Ireson, N.: Does Size Matter? When Small is Good Enough. In: Rowe, M., Stankovic, M., Dadzie, A.-S., Hardey, M. (eds.) Proceedings, 1st Workshop on Making Sense of Microposts (#MSM 2011), pp. 45–56 (May 2011)Google Scholar
  14. 14.
    Gloor, P.A., Laubacher, R., Dynes, S.B.C., Zhao, Y.: Visualization of communication patterns in collaborative innovation networks - analysis of some w3c working groups. In: Proceedings of the Twelfth International Conference on Information and Knowledge Management, CIKM 2003, pp. 56–60. ACM, New York (2003)CrossRefGoogle Scholar
  15. 15.
    Guy, I., Jacovi, M., Perer, A., Ronen, I., Uziel, E.: Same places, same things, same people?: mining user similarity on social media. In: Proceedings of the 2010 ACM Conference on Computer Supported Cooperative Work, CSCW 2010, pp. 41–50. ACM, New York (2010)CrossRefGoogle Scholar
  16. 16.
    Heer, J., Boyd, D.: Vizster: Visualizing online social networks. In: Proceedings of the 2005 IEEE Symposium on Information Visualization, p. 5. IEEE Computer Society, Washington, DC, USA (2005)CrossRefGoogle Scholar
  17. 17.
    Keila, P.S., Skillicorn, D.B.: Structure in the Enron email dataset. Computational & Mathematical Organization Theory 11, 183–199 (2005)CrossRefMATHGoogle Scholar
  18. 18.
    King, D.W., Casto, J., Jones, H.: Communication by Engineers: A Literature Review of Engineers’ Information Needs, Seeking Processes, and Use. Council on Library Resources, Washington (1994)Google Scholar
  19. 19.
    Kossinets, G., Watts, D.J.: Empirical analysis of an evolving social network. Science 311(5757), 88–90 (2006)MathSciNetCrossRefMATHGoogle Scholar
  20. 20.
    Kramár, T.: Towards Contextual Search: Social Networks, Short Contexts and Multiple Personas. In: Konstan, J., Conejo, R., Marzo, J., Oliver, N. (eds.) UMAP 2011. LNCS, vol. 6787, pp. 434–437. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  21. 21.
    Laclavik, M., Dlugolinsky, S., Seleng, M., Kvassay, M., Gatial, E., Balogh, Z., Hluchy, L.: Email analysis and information extraction for enterprise benefit. Computing and Informatics, Special Issue on Business Collaboration Support for Micro, Small, and Medium-Sized Enterprises 30(1), 57–87 (2011)Google Scholar
  22. 22.
    Li, Q., Zheng, Y., Xie, X., Chen, Y., Liu, W., Ma, W.y.: Mining User Similarity Based on Location History. Architecture (c) (2008)Google Scholar
  23. 23.
    Lin, C.Y., Cao, N., Liu, S.X., Papadimitriou, S., Sun, J., Yan, X.: SmallBlue: Social Network Analysis for Expertise Search and Collective Intelligence. In: IEEE 25th International Conference on Data Engineering, ICDE 2009, pp. 1483–1486 (2009)Google Scholar
  24. 24.
    Lin, C.Y., Ehrlich, K., Griffiths-Fisher, V., Desforges, C.: Smallblue: People mining for expertise search. IEEE Multimedia 15, 78–84 (2008)Google Scholar
  25. 25.
    McCallum, A., Wang, X., Corrada-Emmanuel, A.: Topic and role discovery in social networks with experiments on Enron and academic email. Journal of Artificial Intelligence Research 30, 249–272 (2007)Google Scholar
  26. 26.
    Milne, D., Witten, I.H.: Learning to link with wikipedia. In: Proceeding of the 17th ACM Conference on Information and Knowledge Management, CIKM 2008, pp. 509–518. ACM, New York (2008)Google Scholar
  27. 27.
    Mislove, A., Viswanath, B., Gummadi, K.P., Druschel, P.: You are who you know: inferring user profiles in online social networks. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, WSDM 2010, pp. 251–260. ACM, New York (2010)Google Scholar
  28. 28.
    Pazzani, M., Billsus, D.: Learning and revising user profiles: The identification of interesting web sites. Machine Learning 27, 313–331 (1997)CrossRefGoogle Scholar
  29. 29.
    Reingold, E.M., Tilford, J.S.: Tidier drawings of trees. IEEE Transactions on Software Engineering 7, 223–228 (1981)CrossRefGoogle Scholar
  30. 30.
    Robinson, M.A.: Erratum: Correction to robinson, m.a, an empirical analysis of engineers’ information behaviors. Journal of the American Society for Information Science and Technology 61(4), 640–658 (2010); J. Am. Soc. Inf. Sci. Technol. 61, 1947–1947 (September 2010)Google Scholar
  31. 31.
    Schwartz, M.F., Wood, D.C.M.: Discovering shared interests using graph analysis. Communications of the ACM 36(8), 78–89 (1993)CrossRefGoogle Scholar
  32. 32.
    Tuulos, V.H., Perkiö, J., Tirri, H.: Multi-faceted information retrieval system for large scale email archives. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2005, pp. 683–683. ACM, New York (2005)CrossRefGoogle Scholar
  33. 33.
    Tyler, J., Wilkinson, D., Huberman, B.: E-Mail as spectroscopy: Automated discovery of community structure within organizations. The Information Society 21(2), 143–153 (2005)CrossRefGoogle Scholar
  34. 34.
    Viégas, F.B., Donath, J.: Social network visualization: can we go beyond the graph. In: Workshop on Social Networks for Design and Analysis: Using Network Information in CSCW 2004, pp. 6–10 (2004)Google Scholar
  35. 35.
    Xiang, R., Lafayette, W., Lafayette, W.: Modeling Relationship Strength in Online Social Networks. North, 981–990 (2010)Google Scholar
  36. 36.
    Yee, K.P., Fisher, D., Dhamija, R., Hearst, M.: Animated exploration of dynamic graphs with radial layout. In: Proceedings of the IEEE Symposium on Information Visualization 2001 (INFOVIS 2001), p. 43. IEEE Computer Society Press, Washington, DC, USA (2001)Google Scholar
  37. 37.
    Ying, J.J.C., Lu, E.H.C., Lee, W.C., Tseng, V.S.: Mining User Similarity from Semantic Trajectories. Cell, 19–26 (2010)Google Scholar
  38. 38.
    Zhang, Z., Iria, J., Brewster, C., Ciravegna, F.: A comparative evaluation of term recognition algorithms. In: Calzolari, N., Choukri, K., Maegaard, B., Mariani, J., Odjik, J., Piperidis, S., Tapias, D. (eds.) Proceedings of the Sixth International Language Resources and Evaluation (LREC 2008). European Language Resources Association (ELRA), Marrakech (2008)Google Scholar
  39. 39.
    Zhou, Y., Fleischmann, K.R., Wallace, W.A.: Automatic text analysis of values in the Enron email dataset: Clustering a social network using the value patterns of actors. In: HICSS 2010: Proc., 43rd Annual Hawaii International Conference on System Sciences, pp. 1–10 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Anna Lisa Gentile
    • 1
  • Vitaveska Lanfranchi
    • 1
  • Suvodeep Mazumdar
    • 1
  • Fabio Ciravegna
    • 1
  1. 1.Department of Computer ScienceUniversity of SheffieldSheffieldUnited Kingdom

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