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  • Probabilistic Multi-Context Systems

    Author(s)
    Sotomayor, M
    Wang, K
    Shen, Y
    Thornton, J
    Griffith University Author(s)
    Thornton, John R.
    Wang, Kewen
    Sotomayor Sanchez, Marco Vinicio V.
    Year published
    2012
    Metadata
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    Abstract
    The concept of contexts is widely used in artificial intelligence. Several recent attempts have been made to formalize multi-context systems (MCS) for ontology applications. However, these approaches are unable to handle probabilistic knowledge. This paper introduces a formal framework for representing and reasoning about uncertainty in multi-context systems (called p-MCS). Some important properties of p-MCS are presented and an algorithm for computing the semantics is developed. Examples are also used to demonstrate the suitability of p-MCS.The concept of contexts is widely used in artificial intelligence. Several recent attempts have been made to formalize multi-context systems (MCS) for ontology applications. However, these approaches are unable to handle probabilistic knowledge. This paper introduces a formal framework for representing and reasoning about uncertainty in multi-context systems (called p-MCS). Some important properties of p-MCS are presented and an algorithm for computing the semantics is developed. Examples are also used to demonstrate the suitability of p-MCS.
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    Conference Title
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume
    7185 LNCS
    Publisher URI
    http://www.informatik.uni-trier.de/~ley/db/conf/aswc/jist2011.html#SotomayorWST11
    DOI
    https://doi.org/10.1007/978-3-642-29923-0_26
    Subject
    Artificial Intelligence and Image Processing not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/52294
    Collection
    • Conference outputs

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