The USHER System to Generate Semantic Personalised Maps for Travellers

  • Zekeng Liang
  • Kraisak Kesorn
  • Stefan Poslad
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 279)

Abstract

Map applications based upon Geospatial Information Systems (GIS) are seen as a key application area for mobile users, e.g., to enable travellers and mobile assets to be located and tracked, with respect to spatial views, or maps, of destinations and routes. However, current GIS map services tend to lack support for personalisation to: enable users to set preferences based on their context and user profiles; to customise searching and selecting content; to markup maps in-situ forming a personalised spatial memory. For example, current services can’t store, spatial short-cuts, good parking spaces, etc, which have been discovered in-situ, in the physical world. These GIS map services also tend to lack a provision to enable such tagged personal spaces to be used within shared social spaces, i.e., to share spatial memories. An ongoing spatial-aware framework called USHER (Ucommerce Services HEre for Roamers), has been extended, to semantically adapt and personalise maps, and tested. The contributions of this framework are: an ontology-based representation of dynamic user preferences interlinked to a domain model that is able to detect shifts in user interests; the creation of sharable user markup data governed by an access control matrix; the generation of personalised annotated GIS maps.

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References

  1. 1.
    Cheverst, K., Davies, N., Mitchell, K., et al.: Developing a context-aware electronic tourist guide: some issues and experiences. In: Proc. SIGCHI conference on Human factors in computing systems, pp. 17–24 (2000)Google Scholar
  2. 2.
    Freebase, Open, Shared Database of the World’s Knowledge developed by Metaweb, http://www.freebase.com/view/guid/9202a8c04000641f80000000010c2d43 (accessed in May 2008)
  3. 3.
    Göker, A., Myrhaug, H.I.: User Context and Personalisation. In: European Conference on Case-Based Reasoning (ECCBR), pp. 1–7 (2002)Google Scholar
  4. 4.
    Kofod-Petersen, A., Aamodt, A.: Case-based situation assessment in a mobile context-aware system. In: Proc. Artificial intelligence in Mobile Systems 2003 (AIMS), pp. 41–49 (2003)Google Scholar
  5. 5.
    OpenStreetMap, Map Features (2008), http://wiki.openstreetmap.org/index.php/Map_Features (accessed in April 2008)
  6. 6.
    Pignotti, E., Edwards, P., Grimnes, G.A.: Context-Aware Personalised Service Delivery. In: European Conference on Artificial Intelligence, ECAI 2004, pp. 1077–1078 (2004)Google Scholar
  7. 7.
    Poslad, S., Laamanen, H.R., Malaka, A., et al.: CRUMPET: Creation of User-friendly Mobile services PErsonalised for Tourism. In: Proc. 3G 2001 Mobile Communication Technologies, London, pp. 28–32 (2001)Google Scholar
  8. 8.
    Poslad, S.: Ubiquitous Computing: Smart Devices, Environments and Interaction. Wiley, London (2009)Google Scholar
  9. 9.
    Titkov, L., Poslad, S., Tan, J.J.: An Integrated Approach to User-Centered Privacy for Mobile Information Services. Applied Artificial Intelligence 20, 159–178 (2006)CrossRefGoogle Scholar
  10. 10.
    W3C, SPARQL Query Language for RDF, http://www.w3.org/TR/rdf-sparql-query/ (accessed in May 2008)
  11. 11.
    Vallet, D., Castells, P., Fernandez, M., et al.: Personalized Content Retrieval in Context Using Ontological Knowledge. IEEE Transactions on Circuits and Systems for Video Technology 17, 336–346 (2007)CrossRefGoogle Scholar
  12. 12.
    Maes, P.: Agents that reduce work and information overload. Communications of the ACM 37, 30–40 (1994)CrossRefGoogle Scholar
  13. 13.
    Widyantoro, D.H., Ioerger, T.R., Yen, J.: Learning User Interest Dynamics with a Three-Descriptor Representation. Journal of the American Society for Information Science and Technology 52, 212–225 (2001)CrossRefGoogle Scholar
  14. 14.
    Gauch, S., Chaffee, J., Pretschner, A.: Ontology-based personalized search and browsing. Web Intelligent and Agent Systems 1, 219–234 (2003)Google Scholar
  15. 15.
    Liu, F., Yu, C., Meng, W.: Personalized Web Search For Improving Retrieval Effectiveness. IEEE Transaction on Knowledge and Data Engineering 16, 28–40 (2004)CrossRefGoogle Scholar
  16. 16.
    Zhang, Y., Zhang, X., Xu, C., et al.: Personalized retrieval of sports video. In: Proc. of the International Workshop on Multimedia Information Retrieval, pp. 313–322 (2007)Google Scholar
  17. 17.
    Xu, C., Wang, J., Lu, H., et al.: A Novel Framework for Semantic Annotation and Personalized Retrieval of Sports Video. IEEE Transactions on Multimedia 10, 421–436 (2008)CrossRefGoogle Scholar
  18. 18.
    Miller, G.A.: WordNet: a lexical database for English. Communications of the ACM 38, 39–41 (1995)CrossRefGoogle Scholar
  19. 19.
    Castells, P., Fernández, M., Vallet, D., et al.: Self-tuning Personalized Information Retrieval in an Ontology-Based Frame-work. In: OTM Workshops on the Move to Meaningful Internet Systems, pp. 977–986 (2005)Google Scholar
  20. 20.
    Kobsa, L.: Personalised Hypermedia and International Privacy. Communications of the ACM 45(5), 64–67Google Scholar
  21. 21.
    Titkov, L., Poslad, S., Tan, J.J.: Enforcing Privacy via Brokering within Nomadic Environment. In: Proc. of the 4th International Symposium from Agent Theory to Agent Implementation (2004)Google Scholar
  22. 22.
    Castells, P., Fernandez, M., Vallet, D.: An Adaptation of the Vector-Space Model for Ontology-Based Information Retrieval. IEEE Transactions on Knowledge and Data Engineering 19, 261–272 (2007)CrossRefGoogle Scholar
  23. 23.
    Mylonas, P., Vallet, D., Castells, P., et al.: Personalized Information Retrieval Based on Context and Ontological Knowledge. The Knowledge Engineering Review 23, 73–100 (2008)CrossRefGoogle Scholar
  24. 24.
    Daoud, M., Tamine, L., Boughanem, M., et al.: Learning Implicit User Interests Using Ontology and Search History for Personalization. In: Proc. of Web Information Systems Engineering – WISE 2007, pp. 325–336 (2007)Google Scholar
  25. 25.
    Chen, S., Williams, M.: Learning Personalized Ontologies from Text: A Review on an Inherently Transdisciplinary Area. In: Chen, N. (ed.) Personalized Information Retrieval and Access: Concepts, Methods and Practices, New York (2008)Google Scholar
  26. 26.
    Gondra, I.: Personalized Content-Based Image Retrieval. In: Chen, N. (ed.) Personalized Information Retrieval and Access: Concepts, Methods and Practices, New York (2008)Google Scholar
  27. 27.
    Kobayashi, A., Iwamoto, T., Nishiyama, S.: UME: Method for Estimating User Movement Using an Acceleration Sensor. In: Proc. of International Symposium on Applications and the Internet- SAINT 2008, pp. 169–172 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Zekeng Liang
    • 1
  • Kraisak Kesorn
    • 1
  • Stefan Poslad
    • 1
  1. 1.School of Electronic Engineering and Computer Science, Queen MaryUniversity of LondonUK

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