Uncertainty Reasoning for the Semantic Web I ISWC International Workshops, URSW 2005-2007, Revised Selected and Invited Papers /

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Costa, Paulo Cesar G. da (Επιμελητής έκδοσης), d’Amato, Claudia (Επιμελητής έκδοσης), Fanizzi, Nicola (Επιμελητής έκδοσης), Laskey, Kathryn B. (Επιμελητής έκδοσης), Laskey, Kenneth J. (Επιμελητής έκδοσης), Lukasiewicz, Thomas (Επιμελητής έκδοσης), Nickles, Matthias (Επιμελητής έκδοσης), Pool, Michael (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2008.
• Σειρά: Lecture Notes in Computer Science, 5327
• Θέματα: Computer science.; Computer programming.; Mathematical statistics.; Computer science > Mathematics.; Computers.; Artificial intelligence.; Computer Science.; Artificial Intelligence (incl. Robotics).; Computer Science, general.; Programming Techniques.; Models and Principles.; Probability and Statistics in Computer Science.; Math Applications in Computer Science.

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...

Πίνακας περιεχομένων:

• 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.