Buch, Englisch, 403 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 633 g
ISWC International Workshop, URSW 2005-2007, Revised Selected and Invited Papers
Buch, Englisch, 403 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 633 g
Reihe: Lecture Notes in Artificial Intelligence
ISBN: 978-3-540-89764-4
Verlag: Springer Berlin Heidelberg
uncertain. Examples include weather forecasts or gambling odds. Canonical methods for representing and integrating such information are necessaryforcommunicating it ina seamlessfashion.
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Weitere Infos & Material
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.
