Uncertainty Reasoning for the Semantic Web I

This book constitutes the thoroughly refereed first three workshops on Uncertainty Reasoning for the Semantic Web (URSW), held at the International Semantic Web Conferences (ISWC) in 2005, 2006, and 2007.

The 22 papers presented are revised and strongly extended versions of selected workshops papers as well as invited contributions from leading experts in the field and closely related areas. The present volume represents the first comprehensive compilation of state-of-the-art research approaches to uncertainty reasoning in the context of the semantic Web, capturing different models of uncertainty and approaches to deductive as well as inductive reasoning with uncertain formal knowledge.

Klappentext

This book constitutes the thoroughly refereed first three workshops on Uncertainty Reasoning for the Semantic Web (URSW), held at the International Semantic Web Conferences (ISWC) in 2005, 2006, and 2007.

The 22 papers presented are revised and strongly extended versions of selected workshops papers as well as invited contributions from leading experts in the field and closely related areas. The present volume represents the first comprehensive compilation of state-of-the-art research approaches to uncertainty reasoning in the context of the semantic Web, capturing different models of uncertainty and approaches to deductive as well as inductive reasoning with uncertain formal knowledge.

Inhalt
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.