Uncertainty Reasoning for the Semantic Web I
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Uncertainty Reasoning for the Semantic Web I
ISWC International Workshop, URSW 2005-2007, Revised Selected and Invited Papers
Nickles, Matthias; Fanizzi, Nicola; d'Amato, Claudia; Costa, Paulo Cesar G.; Laskey, Kenneth J.; Pool, Michael; Laskey, Kathryn B.
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
12/2008
403
Mole
Inglês
9783540897644
15 a 20 dias
640
Descrição não disponível.
Probabilistic and Dempster-Shafer Models.- Just Add Weights: Markov Logic for the Semantic Web.- Semantic Science: Ontologies, Data and Probabilistic Theories.- Probabilistic Dialogue Models for Dynamic Ontology Mapping.- An Approach to Probabilistic Data Integration for the Semantic Web.- Rule-Based Approaches for Representing Probabilistic Ontology Mappings.- PR-OWL: A Bayesian Ontology Language for the Semantic Web.- Discovery and Uncertainty in Semantic Web Services.- An Approach to Description Logic with Support for Propositional Attitudes and Belief Fusion.- Using the Dempster-Shafer Theory of Evidence to Resolve ABox Inconsistencies.- An Ontology-Based Bayesian Network Approach for Representing Uncertainty in Clinical Practice Guidelines.- Fuzzy and Possibilistic Models.- A Crisp Representation for Fuzzy with Fuzzy Nominals and General Concept Inclusions.- Optimizing the Crisp Representation of the Fuzzy Description Logic .- Uncertainty Issues and Algorithms in Automating Process Connecting Web and User.- Granular Association Rules for Multiple Taxonomies: A Mass Assignment Approach.- A Fuzzy Semantics for the Resource Description Framework.- Reasoning with the Fuzzy Description Logic f- : Theory, Practice and Applications.- Inductive Reasoning and Machine Learning.- Towards Machine Learning on the Semantic Web.- Using Cognitive Entropy to Manage Uncertain Concepts in Formal Ontologies.- Analogical Reasoning in Description Logics.- Approximate Measures of Semantic Dissimilarity under Uncertainty.- Ontology Learning and Reasoning - Dealing with Uncertainty and Inconsistency.- Hybrid Approaches.- Uncertainty Reasoning for Ontologies with General TBoxes in Description Logic.
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Framework;Web Services;bayesian ontology language;cognitive entropy;fuzzy description logic;fuzzy semantics;markov logic;ontologies;ontology mapping;probabilistic dialogue models
Probabilistic and Dempster-Shafer Models.- Just Add Weights: Markov Logic for the Semantic Web.- Semantic Science: Ontologies, Data and Probabilistic Theories.- Probabilistic Dialogue Models for Dynamic Ontology Mapping.- An Approach to Probabilistic Data Integration for the Semantic Web.- Rule-Based Approaches for Representing Probabilistic Ontology Mappings.- PR-OWL: A Bayesian Ontology Language for the Semantic Web.- Discovery and Uncertainty in Semantic Web Services.- An Approach to Description Logic with Support for Propositional Attitudes and Belief Fusion.- Using the Dempster-Shafer Theory of Evidence to Resolve ABox Inconsistencies.- An Ontology-Based Bayesian Network Approach for Representing Uncertainty in Clinical Practice Guidelines.- Fuzzy and Possibilistic Models.- A Crisp Representation for Fuzzy with Fuzzy Nominals and General Concept Inclusions.- Optimizing the Crisp Representation of the Fuzzy Description Logic .- Uncertainty Issues and Algorithms in Automating Process Connecting Web and User.- Granular Association Rules for Multiple Taxonomies: A Mass Assignment Approach.- A Fuzzy Semantics for the Resource Description Framework.- Reasoning with the Fuzzy Description Logic f- : Theory, Practice and Applications.- Inductive Reasoning and Machine Learning.- Towards Machine Learning on the Semantic Web.- Using Cognitive Entropy to Manage Uncertain Concepts in Formal Ontologies.- Analogical Reasoning in Description Logics.- Approximate Measures of Semantic Dissimilarity under Uncertainty.- Ontology Learning and Reasoning - Dealing with Uncertainty and Inconsistency.- Hybrid Approaches.- Uncertainty Reasoning for Ontologies with General TBoxes in Description Logic.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
