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Uncertainty Reasoning for the Semantic Web I

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Cover of 'Uncertainty Reasoning for the Semantic Web I'

Table of Contents

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    Book Overview
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    Chapter 1 Just Add Weights: Markov Logic for the Semantic Web
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    Chapter 2 Semantic Science: Ontologies, Data and Probabilistic Theories
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    Chapter 3 Uncertainty Reasoning for the Semantic Web I
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    Chapter 4 An Approach to Probabilistic Data Integration for the Semantic Web
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    Chapter 5 Rule-Based Approaches for Representing Probabilistic Ontology Mappings
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    Chapter 6 PR-OWL: A Bayesian Ontology Language for the Semantic Web
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    Chapter 7 Discovery and Uncertainty in Semantic Web Services
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    Chapter 8 An Approach to Description Logic with Support for Propositional Attitudes and Belief Fusion
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    Chapter 9 Using the Dempster-Shafer Theory of Evidence to Resolve ABox Inconsistencies
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    Chapter 10 An Ontology-Based Bayesian Network Approach for Representing Uncertainty in Clinical Practice Guidelines
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    Chapter 11 A Crisp Representation for Fuzzy $\cal SHOIN$ with Fuzzy Nominals and General Concept Inclusions
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    Chapter 12 Optimizing the Crisp Representation of the Fuzzy Description Logic $\cal \mathcal{SROIQ}$
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    Chapter 13 Uncertainty Issues and Algorithms in Automating Process Connecting Web and User
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    Chapter 14 Granular Association Rules for Multiple Taxonomies: A Mass Assignment Approach
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    Chapter 15 A Fuzzy Semantics for the Resource Description Framework
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    Chapter 16 Reasoning with the Fuzzy Description Logic f- $\mathcal{SHIN}$ : Theory, Practice and Applications
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    Chapter 17 Towards Machine Learning on the Semantic Web
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    Chapter 18 Using Cognitive Entropy to Manage Uncertain Concepts in Formal Ontologies
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    Chapter 19 Analogical Reasoning in Description Logics
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    Chapter 20 Approximate Measures of Semantic Dissimilarity under Uncertainty
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    Chapter 21 Ontology Learning and Reasoning — Dealing with Uncertainty and Inconsistency
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    Chapter 22 Uncertainty Reasoning for Ontologies with General TBoxes in Description Logic
Attention for Chapter 17: Towards Machine Learning on the Semantic Web
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Chapter title
Towards Machine Learning on the Semantic Web
Chapter number 17
Book title
Uncertainty Reasoning for the Semantic Web I
Published by
Springer, Berlin, Heidelberg, January 2008
DOI 10.1007/978-3-540-89765-1_17
Book ISBNs
978-3-54-089764-4, 978-3-54-089765-1

Volker Tresp, Markus Bundschus, Achim Rettinger, Yi Huang

Mendeley readers

The data shown below were compiled from readership statistics for 98 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 2%
United Kingdom 2 2%
Belgium 1 1%
Germany 1 1%
Australia 1 1%
Spain 1 1%
Greece 1 1%
Colombia 1 1%
Croatia 1 1%
Other 0 0%
Unknown 87 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 28 29%
Student > Master 19 19%
Researcher 18 18%
Student > Doctoral Student 7 7%
Student > Bachelor 7 7%
Other 14 14%
Unknown 5 5%
Readers by discipline Count As %
Computer Science 78 80%
Engineering 5 5%
Business, Management and Accounting 3 3%
Medicine and Dentistry 2 2%
Economics, Econometrics and Finance 1 1%
Other 2 2%
Unknown 7 7%