Probabilistic Multi-Context Systems

Sotomayor, M; Wang, K; Shen, Y; Thornton, J

doi:10.1007/978-3-642-29923-0_26

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

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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