Modeling and Retrieval of Context

Second International Workshop, MRC 2005, Edinburgh, UK, July 31–August 1, 2005, Revised Selected Papers

  • Thomas R. Roth-Berghofer
  • Stefan Schulz
  • David B. Leake

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3946)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 3946)

Table of contents

About these proceedings

Introduction

Computing in context has become a necessity in modern and intelligent IT - plications. With the use of mobile devices and current research on ubiquitous computing, context-awareness has become a major issue. However, context and context-awareness are crucial not only for mobile and ubiquitous computing. They are also vital for spanning various application areas, such as collaborative softwareand Web engineering,personaldigital assistantsand peer-to-peer inf- mation sharing, health care work?ow and patient control, and adaptive games and e-learning solutions. In these areas, context serves as a major source for reasoning, decision making, and adaptation, as it covers not only application knowledge but also environmental knowledge.Likewise, modeling and retrieving context is an important part of modern knowledge management processes. In addition, context can play a role in determining what information a s- tem should provide. This is important for supporting the users of automated or intelligent systems, for tasks such as explaining how solutions are found, what the system is doing, and why it operates in a certain way. The methods applied and the advice given have to be explained, so that the user can understand the process and agree on decisions. Context is equally important for deciding when to provide uncertain or blurred information, e.g., when using a tracking system in situations for which either revealing the current position, or denying access to it, would have adverse e?ects. In this wide range of applications, context is now more than just location.

Keywords

Resolution adaptation algorithmic learning artificial intelligence cognition cognitive science context context extraction context modeling contextual information decision making formal reasoning intelligence knowledge management semantic web

Editors and affiliations

  • Thomas R. Roth-Berghofer
    • 1
  • Stefan Schulz
    • 2
  • David B. Leake
    • 3
  1. 1.Knowledge Management Department, German Research Center for Artificial Intelligence (DFKI) GmbHKaiserslauternGermany
  2. 2.Max-Planck Institut für EisenforschungGermany
  3. 3.Computer Science DepartmentIndiana UniversityBloomingtonU.S.A.

Bibliographic information

  • DOI https://doi.org/10.1007/11740674
  • Copyright Information Springer-Verlag Berlin Heidelberg 2006
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-540-33587-0
  • Online ISBN 978-3-540-33588-7
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • About this book