Titlebook: Uncertainty Reasoning for the Semantic Web III; ISWC International W Fernando Bobillo,Rommel N. Carvalho,Michael Pool Conference proceeding

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evasive

Conference proceedings 2014 from numerous submissions. The papers included in this volume are organized in topical sections on probabilistic and Dempster-Shafer models, fuzzy and possibilistic models, inductive reasoning and machine learning, and hybrid approaches.

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UMP-ST Plug-in: Documenting, Maintaining and Evolving Probabilistic Ontologies Using UnBBayes Frame promising approach to model uncertainty in ontologies, no support has been offered to ontological engineers on how to create this more complex type of ontologies. This task has proven to be extremely difficult and hard, which motivated the creation of the Uncertainty Modeling Process for Semantic T

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Computing Inferences for Relational Bayesian Networks Based on , Constructs,s and roles. We present a restricted version of Credal . that can be viewed as a description language for a class of relational Bayesian networks. The resulting “. networks” offer a simplified and illuminating route both to Credal . and to relational Bayesian networks. We then describe the implement

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,Information Integration with Provenance on the Semantic Web via Probabilistic Datalog+/–,log+/– family of tractable ontology languages is suitable for representing and reasoning over lightweight ontologies, such as . and the . family of description logics. We study how Datalog+/– can be used as a mapping language in the context of information integration. We also provide a complexity an

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