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probabilistic multi-context systems
Sotomayor Marco; Wang Kewen; Shen Yidong; Thornton John
2012
Conference NameJoint International Semantic Technology Conference, JIST 2011
SourceLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages366-375
Conference DateDecember 4, 2011 - December 7, 2011
Conference PlaceHangzhou, China
Indexed TypeEI
ISSN0302-9743
ISBN9783642299223
Department(1) Griffith University Australia; (2) Institute of Software Chinese Academy of Sciences China
English AbstractThe 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. © 2012 Springer-Verlag.; 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. © 2012 Springer-Verlag.
KeywordArtificial Intelligence
Language英语
Content Type会议论文
URIhttp://ir.iscas.ac.cn/handle/311060/15701
Collection中国科学院软件研究所
Recommended Citation
GB/T 7714
Sotomayor Marco,Wang Kewen,Shen Yidong,et al. probabilistic multi-context systems[C],2012:366-375.
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