Uncertainty Reasoning for the Semantic Web I : ISWC International Workshop, URSW 2005-2007, Revised Selected and Invited Papers / edited by Paulo Cesar G. Costa, Claudia d'Amato, Nicola Fanizzi, Kathryn B. Laskey, Kenneth J. Laskey, Matthias Nickles, Michael Pool.

Format:

Book

Contributor:

Costa, Paulo Cesar G., editor.

d'Amato, Claudia, 1977- editor.

Fanizzi, Nicola, editor.

Laskey, Kathryn B., editor.

Laskey, Kenneth J., editor.

Nickles, Matthias, editor.

Pool, Michael, editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Lecture notes in computer science. Lecture notes in artificial intelligence ; 5327.

Lecture Notes in Artificial Intelligence ; 5327

Language:

English

Subjects (All):

Artificial intelligence.

Computer science.

Computer programming.

Computers.

Mathematical statistics.

Computer science--Mathematics.

Artificial Intelligence.

Computer Science, general.

Programming Techniques.

Models and Principles.

Probability and Statistics in Computer Science.

Math Applications in Computer Science.

Local Subjects:

Artificial Intelligence.

Computer Science, general.

Programming Techniques.

Models and Principles.

Probability and Statistics in Computer Science.

Math Applications in Computer Science.

Physical Description:

1 online resource (XIV, 403 pages).

Edition:

First edition 2008.

Contained In:

Springer eBooks

Place of Publication:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2008.

System Details:

text file PDF

Summary:

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.

Contents:

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.

Other Format:

Printed edition:

ISBN:

978-3-540-89765-1

9783540897651

Access Restriction:

Restricted for use by site license.