Uncertainty Reasoning for the Semantic Web 1
(Sprache: Englisch)
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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uncertain. Examples include weather forecasts or gambling odds. Canonical methods for representing and integrating such information are necessaryforcommunicating it ina seamlessfashion.
Inhaltsverzeichnis zu „Uncertainty Reasoning for the Semantic Web 1 “
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.
Bibliographische Angaben
- XIV, 402 Seiten, Maße: 15,5 x 23,5 cm, Kartoniert (TB), Englisch
- Herausgegeben: Paulo Cesar G. da Costa, Claudia d`Amato, Nicola Fanizzi, Kathryn B. Laskey, Kenneth J. Laskey, Thomas Lukasiewicz
- Verlag: Springer Berlin
- ISBN-10: 354089764X
- ISBN-13: 9783540897644
- Erscheinungsdatum: 02.12.2008
Sprache:
Englisch
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