Semantic Knowledge Based Approach for Product Maintenance Support

  • I. Sanya
  • E. Shehab
  • R. Roy
  • O. Houseman
  • M. Jonik
Conference paper


The purpose of this chapter is to use semantic technology to represent the knowledge for product maintenance. Companies are beginning to understand the importance of utilising an effective approach to manage existing knowledge in order to increase their intellectual capital. However, one of the main constraints that have hindered the solution in resolving technical problems has been the efficient access to expertise. Therefore, there is need for enhancing the management and maintenance of knowledge through a semantic based approach. This research project adopts a qualitative research approach and a five-phase research methodology. This research project has highlighted that semantic technology enhances the reusability, flexibility and maintainability of knowledge and its management.


Semantics Ontologies Concepts Rules Knowledge representation 



The authors would like to thank TechModal Ltd for sponsoring this project. They would like also to express their gratitude to all who contributed directly or indirectly to the success of the project.


  1. 1.
    Grover, V., Davenport, T.H. (2001) General perspective on knowledge management: Fostering a research agenda. Journal of Management Information Systems, 18:5–21.CrossRefGoogle Scholar
  2. 2.
    NHS Evidence (2002) KM Technology, IT and knowledge management [Online]. Accessed 21/May/2009.
  3. 3.
    Milton, N. (2007) Knowledge Acquisition in Practice: A Step-by-Step Guide. Springer, London.Google Scholar
  4. 4.
    O’Hara, K., Shadbolt, N. (2001) Managing knowledge capture: economic, technological and methodological considerations. Technical Report, Department of Electronics & Computer Science, University of Southampton.Google Scholar
  5. 5.
    Darlington, K. (2000) The Essence of Expert Systems. Pearson Education.Google Scholar
  6. 6.
    Ulises, C., Ignasi, R., Campos, M., Rollón, C. (2003) Current Reasoning Engine Practice and Integration Strategies. RIMSAT, University of Girona, Girona.Google Scholar
  7. 7.
    Kingston, J. (2004) Conducting feasibility study for knowledge-based systems. Knowledge-Based Systems, 17(2–4):157–164.CrossRefGoogle Scholar
  8. 8.
    Metaxiotis, K. (2004) RECOT: An expert system for the reduction of environmental cost in the textile industry. Information Management & Computer Security, 12(3):218–2731.CrossRefGoogle Scholar
  9. 9.
    Horn, W., Popow, C., Miksch, S., Seyfang, A. (2002) Benefits of a Knowledge-Based System for Parenteral Nutrition Support. Proceedings of 15th European Conference on Artificial Intelligence (ECAI 2002). IOS Press, Amsterdam, pp. 613–617.Google Scholar
  10. 10.
    Reimer, U., Margelisch, A., Staudt, M. (2000) EULE: A knowledge-based system to support business processes. Knowledge-Based Systems, 13(5):261–269.CrossRefGoogle Scholar
  11. 11.
    Nedovic, L., Devedzic, V. (2003) DEVEX: An expert system for currency exchange advising. International Journal of Knowledge-Based Intelligent Engineering Systems, 7(1):38–45.Google Scholar
  12. 12.
    Barski, C. (2003) How to tell stuff to a computer, The Enigmatic art of knowledge representation [Online]. Accessed 25/June/2009.
  13. 13.
    Uschold, M. (2002) A semantic continuum on the semantic web. The Knowledge Engineering Review, 17(1):87–91.CrossRefGoogle Scholar
  14. 14.
    Horrocks, I. (2008, September) Semantic Web, Human-Computer Interaction Series, Chapter 19, (1st Ed.). Springer, London, pp. 315–330.Google Scholar
  15. 15.
    Davies, J., Fensel, D., Harmelen, F.V. (2003) Towards the Semantic Web: Ontology-Driven Knowledge Management. Wiley, New York, NY.Google Scholar
  16. 16.
    NMK (2009) Industry Mixed on Development of Semantic Web [Online]. Accessed 25/June/2009.
  17. 17.
    Agorastos, T., Koutkias, V., Falelakis, M. (2009) Semantic integration of cervical cancer data repositories to facilitate multicenter association studies: The ASSIST Approach. Cancer Informatics, 8:1–44.Google Scholar
  18. 18.
    AXONomics (2008) Financial industry use of semantic technology. Accessed 1/June/2008.
  19. 19.
    Yam, R.C.M., Tse, P.W., Li, L., Tu, P. (2001, February) Intelligent predictive decision support system for condition-based maintenance. The International Journal of Advanced Manufacturing Technology, 17(5):383–391.CrossRefGoogle Scholar
  20. 20.
    Fuchs, F., Henrici, S., Pirker, M., Berger, M., Langer, G., Seitz, C. (2006) Towards semantics-based monitoring of large-scale industrial systems. International Conference on Intelligent Agents, Web Technologies and Internet Commerce, 2006, Sydney, Australia, November, pp. 261–261.Google Scholar
  21. 21.
    TechModal (2009) About us. Accessed 10/January/2010.
  22. 22.
    Ontoprise (2009) SemanticGuide: Intelligent advisory system to support complex consuilting proceses [Online]. Accessed 12/January/2010.

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • I. Sanya
    • 1
  • E. Shehab
    • 2
  • R. Roy
    • 3
  • O. Houseman
    • 4
  • M. Jonik
    • 5
  1. 1.Manufacturing DepartmentCranfield UniversityCranfield BedfordUK
  2. 2.Manufacturing Department, Decision Engineering Centre, School of Applied SciencesCranfield UniversityCranfield, BedfordUK
  3. 3.Manufacturing DepartmentCranfield UniversityCranfield BedfordUK
  4. 4.TechModalBristolUK
  5. 5.TechModalBristolUK

Personalised recommendations