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Paulo CesarG. da Costa, Claudia d’Amato, Nicola Fanizzi, Kathryn B. Laskey, Kenneth J. Laskey, Thomas Lukasiewicz, Matthias Nickles and Michael Pool
Front matter
1-25
Just Add Weights: Markov Logic for the Semantic Web
26-40
Semantic Science: Ontologies, Data and Probabilistic Theories
41-51
Probabilistic Dialogue Models for Dynamic Ontology Mapping
52-65
An Approach to Probabilistic Data Integration for the Semantic Web
66-87
Rule-Based Approaches for Representing Probabilistic Ontology Mappings
88-107
PR-OWL: A Bayesian Ontology Language for the Semantic Web
108-123
Discovery and Uncertainty in Semantic Web Services
124-142
An Approach to Description Logic with Support for Propositional Attitudes and Belief Fusion
143-160
Using the Dempster-Shafer Theory of Evidence to Resolve ABox Inconsistencies
161-173
An Ontology-Based Bayesian Network Approach for Representing Uncertainty in Clinical Practice Guidelines
174-188
A Crisp Representation for Fuzzy SHOIN\cal SHOIN with Fuzzy Nominals and General Concept Inclusions
189-206
Optimizing the Crisp Representation of the Fuzzy Description Logic SROIQ\cal \mathcal{SROIQ}
207-223
Uncertainty Issues and Algorithms in Automating Process Connecting Web and User
224-243
Granular Association Rules for Multiple Taxonomies: A Mass Assignment Approach
244-261
A Fuzzy Semantics for the Resource Description Framework
262-281
Reasoning with the Fuzzy Description Logic f- SHIN\mathcal{SHIN}: Theory, Practice and Applications
282-314
Towards Machine Learning on the Semantic Web
315-329
Using Cognitive Entropy to Manage Uncertain Concepts in Formal Ontologies
330-347
Analogical Reasoning in Description Logics
348-365
Approximate Measures of Semantic Dissimilarity under Uncertainty
366-384
Ontology Learning and Reasoning — Dealing with Uncertainty and Inconsistency
385-402
Uncertainty Reasoning for Ontologies with General TBoxes in Description Logic
Back matter
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