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.