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Discovering Mobile Social Networks by Semantic Technologies

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Handbook of Social Network Technologies and Applications

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Abstract

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.

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Notes

  1. 1.

    It is a research project called NICE for delivering personalized information to mobile devices via the social networks. Real customer information has been provided from KT Freetel (KTF), one of the major telecommunication companies in Korea.

References

  1. Tim Berners-Lee. The semantic web. Scientific American, 285(5):34–43, 2001

    Article  Google Scholar 

  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, 2006

    Google Scholar 

  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, 2003

    Google Scholar 

  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, 2004

    Google Scholar 

  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, 2001

    Google Scholar 

  6. Nathan Eagle and Alex Pentland. Reality mining: sensing complex social systems. Personal and Ubiquitous Computing, 10(4):255–268, 2006

    Article  Google Scholar 

  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, 2004

    Google Scholar 

  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, 2000

    Google Scholar 

  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, 2001

    Google Scholar 

  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, 1997

    Article  Google Scholar 

  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, 1988

    Article  MathSciNet  Google Scholar 

  12. Jason J. Jung. Ontological framework based on contextual mediation for collaborative information retrieval. Information Retrieval, 10(1):85–109, 2007

    Article  Google Scholar 

  13. Jason J. Jung. Ontology-based context synchronization for ad-hoc social collaborations. Knowledge-Based Systems, 21(7):573–580, 2008

    Article  Google Scholar 

  14. Jason J. Jung. Contextualized mobile recommendation service based on interactive social network discovered from mobile users. Expert Systems with Applications, 36:11950–11956, 2009

    Article  Google Scholar 

  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, 2007

    Google Scholar 

  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, 2009

    Google Scholar 

  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, 2007

    Google Scholar 

  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, 2001

    Google Scholar 

  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, 1997

    Article  Google Scholar 

  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, 2003

    Article  Google Scholar 

  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, 2005

    Article  Google Scholar 

  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, 2001

    Google Scholar 

  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, 2008

    Article  Google Scholar 

  24. Gautam Pant and Padmini Srinivasan. Link contexts in classifier-guided topical crawlers. IEEE Transactions on Knowledge and Data Engineering, 18(1):107–122, 2006

    Article  Google Scholar 

  25. Michael J. Pazzani. A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review, 13(5-6):393–408, 1999

    Article  Google Scholar 

  26. Christelle Petit-Rozé and Emmanuelle Grislin-Le Strugeon. MAPIS, a multi-agent system for information personalization. Information & Software Technology, 48(2):107–120, 2006

    Article  Google Scholar 

  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, 2007

    Google Scholar 

  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, 2004

    Google Scholar 

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Acknowledgements

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.

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Authors and Affiliations

  1. Yeungnam University, Dae-Dong Gyeongsan, Korea, 712-749

    Jason J. Jung

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  1. Jason J. Jung
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  2. Kwang Sun Choi
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  3. Sung Hyuk Park
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Corresponding author

Correspondence to Jason J. Jung .

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Editors and Affiliations

  1. Dept. of Comp. & Elect. Engin. and, Florida Atlantic University, Glades Road 777, Boca Raton, 33431, Florida, USA

    Borko Furht

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Jung, J.J., Choi, K.S., Park, S.H. (2010). Discovering Mobile Social Networks by Semantic Technologies. In: Furht, B. (eds) Handbook of Social Network Technologies and Applications. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7142-5_10

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  • DOI: https://doi.org/10.1007/978-1-4419-7142-5_10

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