LiteMap: An Ontology Mapping Approach for Mobile Agents’ Context-Awareness

  • Haïfa Zargayouna
  • Nejla Amara
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4278)


Mobile agents’ applications have to operate within environments having continuously changing execution conditions that are not easily predictable. They have to dynamically adapt to changes in their context resulting from other’s activities and resources variation. To be aware of their execution context, mobile agents require a formal and structured model of the context and a reasoning process for detecting suspect situations. In this work, we use formal ontologies to model the agents’ execution context as well as its composing elements. After each agent migration, a reasoning process is carried out upon these ontologies to detect meaningful environmental changes. This reasoning is based on semantic web techniques for mapping ontologies. The output of the mapping process is a set of semantic relations among the ontologies concepts that will be used by the agent to trigger a reconfiguration of its structure according to some adaptation policies.


Mobile Agent Execution Environment Adaptation Policy Formal Ontology Agent Structure 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Amara-Hachmi, N., Fallah-Seghrouchni, A.E.: A framework for context-aware mobile agents. In: 4th Workshop on Ambient Intelligence - Agents for Ubiquitous Environments held in conjunction with AAMAS 2005 (2005)Google Scholar
  2. 2.
    Salber, A.D.D., Abowd, G.: A conceptual framework and a toolkit for supporting the rapid prototyping of context-aware applications. Human- Computer Interaction (HCI) Journal 16(2-4), 97–166 (2001)Google Scholar
  3. 3.
    Berners-Lee, T., Hendler, J., Lassila, O.: The semantic web: A new form of web content that is meaningful to computers will unleash a revolution of new possibilities. Scientific American, 35–43 (2001)Google Scholar
  4. 4.
    Dean, M., Schreiber, G. (eds.): Owl web ontology language: reference, Recommendation, W3C (2004),
  5. 5.
    Chen, H., et al.: An ontology for context-aware pervasive computing environments. Special Issue on Ontologies for Distributed Systems, Knowledge Engineering Review (2004)Google Scholar
  6. 6.
    Wang, X.H., et al.: Ontology based context modeling and reasoning using owl. In: Workshop on Context Modeling and Reasoning at IEEE International Conference on Pervasive Computing and Communication (PerCom 2004) (2004)Google Scholar
  7. 7.
    Euzenat, J., Bach, T.L., Barrasa, J., Bouquet, P., Bo, J.D., Dieng-Kuntz, R., Ehrig, M., Hauswirth, M., Jarrar, M., Lara, R., Maynard, D., Napoli, A., Stamou, G., Stuckenschmidt, H., Shvaiko, P., Tessaris, S., Acker, S.V., Zaihrayeu, I.: State of the art on ontology alignment (2004)Google Scholar
  8. 8.
    MacGregor, R., Chalupsky, H., Moriarty, D., Valente, A.: Ontology merging with ontomorph (1999),
  9. 9.
    Noy, N., Musen, M.: Prompt: Algorithm and tool for automated ontology merging and alignment. In: Seventeenth National Conference on Artificial Intelligence (AAAI 2000) (2000)Google Scholar
  10. 10.
    Stumme, G., Madche, A.: Fca-merge: Bottom-up merging of ontologies. In: 7th Intl. Conf. on Artificial Intelligence (IJCAI 2001), pp. 225–230 (2001)Google Scholar
  11. 11.
    Kalfoglou, Y., Schorlemmer, M.: If-map: An ontology-mapping method based on information-flow theory. Journal of Data Semantics 1(1) (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Haïfa Zargayouna
    • 1
  • Nejla Amara
    • 1
  1. 1.LIPN, Université Paris 13 – CNRS UMR 7030VilletaneuseFrance

Personalised recommendations