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Inferring Affordances Using Learning Techniques

  • Amel Bennaceur
  • Richard Johansson
  • Alessandro Moschitti
  • Romina Spalazzese
  • Daniel Sykes
  • Rachid Saadi
  • Valérie Issarny
Part of the Communications in Computer and Information Science book series (CCIS, volume 255)

Abstract

Interoperability among heterogeneous systems is a key challenge in today’s networked environment, which is characterised by continual change in aspects such as mobility and availability. Automated solutions appear then to be the only way to achieve interoperability with the needed level of flexibility and scalability. While necessary, the techniques used to achieve interaction, working from the highest application level to the lowest protocol level, come at a substantial computational cost, especially when checks are performed indiscriminately between systems in unrelated domains. To overcome this, we propose to use machine learning to extract the high-level functionality of a system and thus restrict the scope of detailed analysis to systems likely to be able to interoperate.

Keywords

Support Vector Machine Service Composition Automatic Docu Semantic Match Interface Description 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Amel Bennaceur
    • 1
  • Richard Johansson
    • 2
  • Alessandro Moschitti
    • 2
  • Romina Spalazzese
    • 3
  • Daniel Sykes
    • 1
  • Rachid Saadi
    • 1
  • Valérie Issarny
    • 1
  1. 1.INRIAParis-RocquencourtFrance
  2. 2.University of TrentoItaly
  3. 3.University of L’AquilaL’AquilaItaly

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