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.
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Sotomayor, M., Wang, K., Shen, Y., Thornton, J. (2012). Probabilistic Multi-Context Systems. In: Pan, J.Z., et al. The Semantic Web. JIST 2011. Lecture Notes in Computer Science, vol 7185. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29923-0_26
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DOI: https://doi.org/10.1007/978-3-642-29923-0_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29922-3
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