Discovering Mobile Social Networks by Semantic Technologies



It has been important for telecommunication companies to discover social networks from mobile subscribers. They have attempted to provide a number of recommendation services, but they realized that the services were not successful. In this chapter, we present semantic technologies for discovering social networks. The process is mainly composed of two steps; (1) profile identification and (2) context understanding. Through developing a Next generation Contents dElivery (NICE) platform, we were able to generate various services based on the discovered social networks.


Social Network Mobile User Contextual Dependency Recommendation Service Mobile Subscriber 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported by the Korean Science and Engineering Foundation (KOSEF) grant funded by the Korean government (MEST). (2008-0058292). This chapter has been significantly revised from the paper [14] published in Expert Systems with Applications, Vol. 36 (pp. 11950–11956) in 2009.


  1. 1.
    Tim Berners-Lee. The semantic web. Scientific American, 285(5):34–43, 2001CrossRefGoogle Scholar
  2. 2.
    Philip Bonhard, Clare Harries, John McCarthy, and M. Angela Sasse. Accounting for taste: using profile similarity to improve recommender systems. In CHI ’06: Proceedings of the SIGCHI conference on Human Factors in computing systems, pages 1057–1066, ACM, NY, 2006Google Scholar
  3. 3.
    Giacomo Cabri, Letizia Leonardi, Marco Mamei, and Franco Zambonelli. Location-dependent services for mobile users. IEEE Transactions on Systems, Man, and Cybernetics – Part A: Systems and Humans, 33(6):667–681, 2003Google Scholar
  4. 4.
    S. Wesley Changchien, Chin-Feng Lee, and Yu-Jung Hsu. On-line personalized sales promotion in electronic commerce. Expert Systems with Applications, 27(1):35–52, 2004Google Scholar
  5. 5.
    Rob Cross, Ronald E. Rice, and Andrew Parker. Information seeking in social context: Structural influences and receipt of information benefits. IEEE Transactions on Systems, Man, and Cybernetics - Part C: Applications and Reviews, 31(4):438–448, 2001Google Scholar
  6. 6.
    Nathan Eagle and Alex Pentland. Reality mining: sensing complex social systems. Personal and Ubiquitous Computing, 10(4):255–268, 2006CrossRefGoogle Scholar
  7. 7.
    Jérôme Euzenat and Petko Valtchev. Similarity-based ontology alignment in OWL-Lite. In Ramon López de Mántaras and Lorenza Saitta, editors, Proceedings of the 16th European Conference on Artificial Intelligence (ECAI’2004), Valencia, Spain, August 22-27, 2004, pages 333–337. IOS Press, 2004Google Scholar
  8. 8.
    Jonathan L. Herlocker, Joseph A. Konstan, and John Riedl. Explaining collaborative filtering recommendations. In CSCW ’00: Proceedings of the 2000 ACM conference on Computer supported cooperative work, pages 241–250, ACM, NY, 2000Google Scholar
  9. 9.
    Francisco Herrera and Luis Martínez. A model based on linguistic 2-tuples for dealing with multigranular hierarchical linguistic contexts in multi-expert decision-making. IEEE Transactions on Systems, Man, and Cybernetics – Part B: Cybernetics, 31(2):227–234, 2001Google Scholar
  10. 10.
    Tzung-Pei Hong and Shian-Shyong Tseng. A generalized version space learning algorithm for noisy and uncertain data. IEEE Transactions on Knowledge and Data Engineering, 9(2):336–340, 1997CrossRefGoogle Scholar
  11. 11.
    R. H. Irving and D. W. Conrath. The social context of multiperson, multiattribute decisionmaking. IEEE Transactions on Systems, Man and Cybernetics, 18(3):348–357, 1988CrossRefMathSciNetGoogle Scholar
  12. 12.
    Jason J. Jung. Ontological framework based on contextual mediation for collaborative information retrieval. Information Retrieval, 10(1):85–109, 2007CrossRefGoogle Scholar
  13. 13.
    Jason J. Jung. Ontology-based context synchronization for ad-hoc social collaborations. Knowledge-Based Systems, 21(7):573–580, 2008CrossRefGoogle Scholar
  14. 14.
    Jason J. Jung. Contextualized mobile recommendation service based on interactive social network discovered from mobile users. Expert Systems with Applications, 36:11950–11956, 2009CrossRefGoogle Scholar
  15. 15.
    Jason J. Jung and Jérôme Euzenat. Towards semantic social networks. In Enrico Franconi, Michael Kifer, and Wolfgang May, editors, Proceedings of the 4th European Semantic Web Conference (ESWC 2007), Innsbruck, Austria, volume 4519 of Lecture Notes in Computer Science, pages 267–280. Springer, Berlin, 2007Google Scholar
  16. 16.
    Jason J. Jung, Hojin Lee, and Kwang Sun Choi. Contextualized recommendation based on reality mining from mobile subscribers. Cybernetics and Systems, 40(2):160–175, 2009Google Scholar
  17. 17.
    Przemyslaw Kazienko. Expansion of telecommunication social networks. In Yuhua Luo, editor, Proceedings of the 4th International Conference on Cooperative Design, Visualization, and Engineering (CDVE 2007), volume 4674 of Lecture Notes in Computer Science, pages 404–412. Springer, Berlin, 2007Google Scholar
  18. 18.
    Jon M. Kleinberg. Small-world phenomena and the dynamics of information. In Thomas G. Dietterich, Suzanna Becker, and Zoubin Ghahramani, editors, Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, NIPS 2001, December 3-8, 2001, Vancouver, British Columbia, Canada], pages 431–438. MIT, MA, 2001Google Scholar
  19. 19.
    Joseph A. Konstan, Bradley N. Miller, David Maltz, Jonathan L. Herlocker, Lee R. Gordon, and John Riedl. Grouplens: applying collaborative filtering to usenet news. Communications of the ACM, 40(3):77–87, 1997CrossRefGoogle Scholar
  20. 20.
    Panu Korpipaa, Jani Mantyjarvi, Juha Kela, Heikki Keranen, and Esko-Juhani Malm. Managing context information in mobile devices. IEEE Pervasive Computing, 2(3):42–51, 2003CrossRefGoogle Scholar
  21. 21.
    Ohbyung Kwon, Sungchul Choi, and Gyuro Park. NAMA: a context-aware multi-agent based web service approach to proactive need identification for personalized reminder systems. Expert Systems with Applications, 29(1):17–32, 2005CrossRefGoogle Scholar
  22. 22.
    Farid MELGANI, Sebastiano B. SERPICO, and Gianni VERNAZZA. Fusion of multitemporal contextual information by neural networks for multisensor image classification. In Proceedings of the 2001 IEEE International Geoscience and Remote Sensing Symposium (IGARSS ’01), pages 2952–2954. IEEE Computer Society, 2001Google Scholar
  23. 23.
    Gerald Mollenhorst, Beate Völker, and Henk Flap. Social contexts and personal relationships: The effect of meeting opportunities on similarity for relationships of different strength. Social Networks, 30(1):60–68, 2008CrossRefGoogle Scholar
  24. 24.
    Gautam Pant and Padmini Srinivasan. Link contexts in classifier-guided topical crawlers. IEEE Transactions on Knowledge and Data Engineering, 18(1):107–122, 2006CrossRefGoogle Scholar
  25. 25.
    Michael J. Pazzani. A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review, 13(5-6):393–408, 1999CrossRefGoogle Scholar
  26. 26.
    Christelle Petit-Rozé and Emmanuelle Grislin-Le Strugeon. MAPIS, a multi-agent system for information personalization. Information & Software Technology, 48(2):107–120, 2006CrossRefGoogle Scholar
  27. 27.
    Odysseas Sekkas, Christos B. Anagnostopoulos, and Stathes Hadjiefthymiades. Context fusion through imprecise reasoning. In Proceedings of the 2007 IEEE International Conference on Pervasive Services (ICPS), pages 88–91. IEEE Computer Society, 2007Google Scholar
  28. 28.
    Thomas Strang and Claudia LinnhoffPopien. A context modeling survey. In Proceedings of the Workshop on Advanced Context Modelling, Reasoning and Management colocationed with UbiComp 2004, 2004Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Jason J. Jung
    • 1
  • Kwang Sun Choi
  • Sung Hyuk Park
  1. 1.Yeungnam UniversityDae-Dong GyeongsanKorea

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