Skip to main content
SpringerLink
Log in

Using Ontologies for Managing User Profiles in Personalised Mobile Service Delivery

  • Chapter
Book cover Health Monitoring and Personalized Feedback using Multimedia Data

  • 754 Accesses

Abstract

We are now living in a technological world where the adoption of pervasive technologies is becoming more prevalent. This has sparked growth in the development of services for delivery in pervasive environments, across a number of application domains including healthcare. User personalisation, in particular, has become an important element for delivering pervasive healthcare, which has coincided with the rapid increase in the use of smart-phone technologies. Increased user dependence on technology has resulted in a need to provide personalised service delivery, in the form of adaptive technology. Many studies have explored the use of ontological user modelling techniques to facilitate mobile service personalisation. Ontological user models have been developed for use within personalised web information retrieval systems, adaptive user interface design and within public services. Nevertheless, these models have not been adopted to implement the personalisation of assistive services for mobile users within pervasive environments. Every person is unique and therefore, will exhibit unique behaviours, wants and needs, which will also change over time. Adaptive technologies must be able to cater for human behavioural changes, and change to suit them via on-demand service delivery. This Chapter focuses on two key perspectives. Firstly, the modelling of different users within pervasive environments is introduced and critiqued and secondly, the topics of ontological modelling and user profile representation are contextualised within a discussion surrounding previous research undertaken by the authors.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Cook, D. J., & Das, S. K. (2007). How smart are our environments? An updated look at the state of the art. Pervasive and Mobile Computing, 3(2), 53–73.

    Article  Google Scholar 

  2. Bharucha, A. J., Anand, V., Forlizzi, J., Dew, M. A., Reynolds III, C. F., Stevens, S., et al. (2009). Intelligent assistive technology applications to dementia care: Current capabilities, limitations, and future challenges. American Journal of Geriatric Psychiatry, 17(2), 88–104.

    Article  Google Scholar 

  3. Skillen, K.-L., Chen, L., Nugent, C. D., Donnelly, M. P., Burns, W., & Solheim, I. (2013). Ontological user modelling and semantic rule-based reasoning for personalisation of help-on-demand services in pervasive environments. Future Generation Computer Systems, 34, 97–109.

    Article  Google Scholar 

  4. Zimmermann, A., Specht, M., & Lorenz, A. (2005). Personalization and context management. User Modeling and User-Adapted Interaction, 15(3–4), 275–302.

    Article  Google Scholar 

  5. Roh, J. H., & Jin, S. (2012). Personalized advertisement recommendation system based on user profile in the smart phone. In 2012 14th International Conference on Advanced Communication Technology (ICACT) (pp. 1300–1303).

    Google Scholar 

  6. Goix, L.-W., Valla, M., Cerami, L., & Falcarin, P. (2007). Situation inference for mobile users: A rule based approach. In 2007 International Conference on. Mobile Data Management (pp. 299–303).

    Google Scholar 

  7. Mikalsen, M., & Kofod-Petersen, A. (2004). Representing and reasoning about context in a mobile environment. In Proceedings of the First International Workshop on Modeling and Retrieval of Context (pp. 25–35).

    Google Scholar 

  8. Schilit, B., Adams, N., & Want, R. (1994). Context-aware computing applications. In First Workshop on Mobile Computing Systems and Applications, WMCSA 1994 (Vol. 1994, pp. 85–90).

    Google Scholar 

  9. Hong, J., Suh, E., & Kim, S. ((2009). Expert systems with applications context-aware systems? A literature review and classification. Expert Systems with Applications, 36(4), 8509–8522.

    Google Scholar 

  10. Bettini, C., Brdiczka, O., Henricksen, K., Indulska, J., Nicklas, D., Ranganathan, A., et al. (2010). A survey of context modelling and reasoning techniques. Pervasive and Mobile Computing, 6(2), 161–180.

    Article  Google Scholar 

  11. Want, R. (2013). Keynote 1: The golden age of pervasive computing. In 2013 IEEE International Conference on Pervasive Computing and Communications (PerCom) (p. 1).

    Google Scholar 

  12. Baldauf, M., Dustdar, S., & Rosenberg, F. A survey on context-aware systems. International Journal of Ad Hoc and Ubiquitous Computing, 2(4), 263–277.

    Google Scholar 

  13. Hoareau, C., & Satoh, I. (2009). Modeling and processing information for context-aware computing: A survey. New Generation Computing, 27(3), 177–196.

    Article  MATH  Google Scholar 

  14. Chen, L., Nugent, C., & Wang, H. (2011). A knowledge-driven approach to activity recognition in smart homes. IEEE Transactions on Knowledge and Data Engineering, PP(99), 1.

    Google Scholar 

  15. Rodríguez, J., Bravo, M., & Guzmán, R. (2013). Multi-dimensional ontology model to support context-aware systems. In Workshops at the Twenty-Seventh AAAI Conference on Artificial Intelligence, March 2012 (pp. 53–60).

    Google Scholar 

  16. Fischer, G. (2001). User modeling in human-computer interaction. User Modeling and User-Adapted Interaction, 11(1–2), 65–86.

    Article  MATH  Google Scholar 

  17. Kuflik, T., Kay, J., & Kummerfeld, B. (2012). Challenges and solutions of ubiquitous user modeling. In Ubiquitous display environments (pp. 7–30). New York: Springer.

    Chapter  Google Scholar 

  18. Rojbi, S., & Soui, M. (2011). User modeling and web-based customazation techniques: An examination of the published literature. In 2011 4th International Conference on Logistics (LOGISTIQUA) (pp. 83–90).

    Google Scholar 

  19. Ye, J., Dobson, S., & McKeever, S. (2012). Situation identification techniques in pervasive computing: A review. Pervasive and Mobile Computing, 8(1), 36–66.

    Article  Google Scholar 

  20. Adomavicius, G., & Tuzhilin, A. (2005). 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(6), 734–749.

    Article  Google Scholar 

  21. Held, A., Buchholz, S., & Schill, A. (2002). Modeling of context information for pervasive computing applications. In Proceedings of SCI.

    Google Scholar 

  22. Carmagnola, F., Cena, F., & Gena, C. (2011). User model interoperability: A survey. User Modeling and User-Adapted Interaction, 21(3), 285–331.

    Article  Google Scholar 

  23. Liu, C. H., Chang, K. L., Chen, J. J. Y., & Hung, S. C. (2010). Ontology-based context representation and reasoning using owl and swrl. In 2010 Eighth Annual Communication Networks and Services Research Conference (CNSR) (pp. 215–220).

    Google Scholar 

  24. Andrejko, A., Barla, M., & Bieliková, M. (2007,). Ontology-based user modeling for web-based information systems. In Advances in Information Systems Development (pp. 457–468). New York, Springer.

    Google Scholar 

  25. Zukerman, I., & Albrecht, D. W. (2001). Predictive statistical models for user modeling. User Modeling and User-Adapted Interaction, 11(1–2), 5–18.

    Article  MATH  Google Scholar 

  26. Skillen, K. L., Chen, L., Nugent, C. D., Donnelly, M. P., & Solheim, I. (2012). A user profile ontology based approach for assisting people with dementia in mobile environments. In Conference Proceedings of IEEE Engineering in Medicine and Biology Society (Vol. 2012, pp. 6390–6393).

    Google Scholar 

  27. Viviani, M., Bennani, N., & Egyed-Zsigmond, E. (2010). A survey on user modeling in multi-application environments. In 2010 Third International Conference on Advances in Human-Oriented and Personalized Mechanisms, Technologies and Services (CENTRIC) (pp. 111–116).

    Google Scholar 

  28. Noy, N. F., & McGuinness, D. L. (2001). Ontology development 101: A guide to creating your first ontology. Stanford, CA: Stanford University.

    Google Scholar 

  29. S. C. for B. I. Research, “Protégé - A free, open-source ontology editor and framework for building intelligent systems.” [Online]. Available: http://protege.stanford.edu/.[Accessed:22-Sep-2014]

  30. Eyharabide, V., & Amandi, A. (2012). Ontology-based user profile learning. Applied Intelligence, 36(4), 857–869.

    Article  Google Scholar 

  31. Kobsa, A. (2001). Generic user modeling systems. User Modeling and User-Adapted Interaction, 11(1), 49–63.

    Article  MATH  Google Scholar 

  32. Pan, J., Zhang, B., Wang, S., Wu, G., & Wei, D. (2007). Ontology based user profiling in personalized information service agent. In 7th IEEE International Conference on Computer and Information Technology, CIT 2007 (pp. 1089–1093)

    Google Scholar 

  33. Heckmann, D., Schwartz, T., Brandherm, B., Schmitz, M., & von Wilamowitz-Moellendorff, M. (2005). Gumo-the general user model ontology. In User modeling (pp. 428–432). Heidelberg: Springer.

    Google Scholar 

  34. Sutterer, M., Droegehorn, O., & David, K. (2008). Upos: User profile ontology with situation-dependent preferences support. In 2008 First International Conference on Advances in Computer-Human Interaction (pp. 230–235).

    Google Scholar 

  35. Von Hessling, A., Kleemann, T., & Sinner, A. (2004). Semantic user profiles and their applications in a mobile environment. Artificial Intelligence in Mobile Systems (AIMS 2004) (p. 59).

    Google Scholar 

  36. Burns, W., Chen, L., Nugent, C., Donnelly, M., Skillen, K.-L., & Solheim, I. (2012). A conceptual framework for supporting adaptive personalized help-on-demand services. In Ambient Intelligence (pp. 427–432). Berlin: Springer.

    Chapter  Google Scholar 

  37. Halbach, T. (2013). The European MobileSage project - situated adaptive guidance for the mobile elderly overview, status, and preliminary results. In The Sixth International Conference on Advances in Computer-Human Interactions (pp. 479–482).

    Google Scholar 

  38. Gao, M., Liu, K., & Wu, Z. (2010). Personalisation in web computing and informatics: Theories, techniques, applications, and future research. Information Systems Frontiers, 12(5), 607–629.

    Article  Google Scholar 

  39. Razmerita, L. (2011). An ontology-based framework for modeling user behavior?A case study in knowledge management. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 41(4), 772–783.

    Article  Google Scholar 

  40. Horrocks, I., Patel-Schneider, P. F., Boley, H., Tabet, S., Grosof, B., & Dean, M. (2004). SWRL: A semantic web rule language combining OWL and RuleML. W3C Member Submissions ,21, 79.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Ulster, London, UK

    Kerry-Louise Skillen & Chris Nugent

  2. University of Ulster, Northern Ireland, UK

    Mark Donnelly, Liming Chen & William Burns

Authors
  1. Kerry-Louise Skillen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chris Nugent
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mark Donnelly
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Liming Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. William Burns
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kerry-Louise Skillen .

Editor information

Editors and Affiliations

  1. Centre for Research and Technology, Hellas, Information Technologies Institute, Thermi, Thessaloniki, Greece

    Alexia Briassouli

  2. Laboratoire Bordelais de Recherche en Informatique/University Bordeaux, Talence, France

    Jenny Benois-Pineau

  3. Computer Science Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA

    Alexander Hauptmann

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Skillen, KL., Nugent, C., Donnelly, M., Chen, L., Burns, W. (2015). Using Ontologies for Managing User Profiles in Personalised Mobile Service Delivery. In: Briassouli, A., Benois-Pineau, J., Hauptmann, A. (eds) Health Monitoring and Personalized Feedback using Multimedia Data. Springer, Cham. https://doi.org/10.1007/978-3-319-17963-6_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-17963-6_13

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-17962-9

  • Online ISBN: 978-3-319-17963-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation