By Fernando Bobillo, Rommel N. Carvalho, Paulo C.G. Costa, Claudia d'Amato, Nicola Fanizzi, Kathryn B. Laskey, Kenneth J. Laskey, Thomas Lukasiewicz, Matthias Nickles, Michael Pool
This ebook comprises revised and considerably prolonged types of chosen papers from 3 workshops on Uncertainty Reasoning for the Semantic internet (URSW), held on the overseas Semantic internet meetings (ISWC) in 2011, 2012, and 2013. The sixteen papers awarded have been rigorously reviewed and chosen from a variety of submissions. The papers incorporated during this quantity are prepared in topical sections on probabilistic and Dempster-Shafer types, fuzzy and possibilistic versions, inductive reasoning and laptop studying, and hybrid approaches.
Read or Download Uncertainty Reasoning for the Semantic Web III: ISWC International Workshops, URSW 2011-2013, Revised Selected Papers PDF
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Extra resources for Uncertainty Reasoning for the Semantic Web III: ISWC International Workshops, URSW 2011-2013, Revised Selected Papers
Concept inclusions/definitions are denoted respectively by C D and C ≡ D, where C and D are concepts. Concept C ¬C is denoted by , and concept C ¬C is denoted by ⊥. Restrictions ∃r. and ∀r. are abbreviated by ∃r and ∀r respectively. A set of concept inclusions and deﬁnitions is a terminology. If an inclusion/deﬁnition contains a concept C in its left hand side and a concept D in its right hand side, C directly uses D. Indicate the transitive closure of directly uses by uses. A terminology is acyclic if it is a set of concept inclusions/deﬁnitions such that no concept in the terminology uses itself .
Sci. Technol. 7(58), 1019–1031 (2007) 41. : Probabilistic description logic programs. Int. J. Approx. Reason. 45(2), 288–307 (2007) Computing Inferences for Relational Bayesian Networks 39 42. : Expressive probabilistic description logics. Artif. Intell. 172(6–7), 852–883 (2008) 43. : Tightly integrated probabilistic description logic programs for representing ontology mappings. Ann. Math. Artif. Intell. 63(3/4), 385–425 (2011) 44. : Managing uncertainty and vagueness in description logics for the semantic web.
Kisynski and Poole’s AC-FOVE algorithm, an extension of ﬁrst-order variable elimination [33,34], is currently the state-of-art for lifted inference with aggregation parfactors. We still focus on ﬁnite domains, and in this case each construct in a crALC network can be translated either into parameterized functions or into aggregation parfactors. C can be encoded into an aggregation parfactor that yields 1 when the number of instances of r(x, y) ∧ C(y) is larger than one, and 0 otherwise. AC-FOVE then applies a set of rules to the functions and parfactors.