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Uncertainty Reasoning for the Semantic Web I: ISWC International Workshop, URSW 2005-2007, Revised Selected and Invited Papers 2008 ed. [Mīkstie vāki]

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  • Formāts: Paperback / softback, 403 pages, height x width: 235x155 mm, weight: 640 g, XIV, 403 p., 1 Paperback / softback
  • Sērija : Lecture Notes in Computer Science 5327
  • Izdošanas datums: 02-Dec-2008
  • Izdevniecība: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 354089764X
  • ISBN-13: 9783540897644
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  • Formāts: Paperback / softback, 403 pages, height x width: 235x155 mm, weight: 640 g, XIV, 403 p., 1 Paperback / softback
  • Sērija : Lecture Notes in Computer Science 5327
  • Izdošanas datums: 02-Dec-2008
  • Izdevniecība: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 354089764X
  • ISBN-13: 9783540897644
Citas grāmatas par šo tēmu:
Thisvolumecontainstheproceedingsofthe rstthreeworkshopsonUncertainty Reasoning for the Semantic Web (URSW), held at the International Semantic Web Conferences (ISWC) in 2005, 2006, and 2007. In addition to revised and stronglyextendedversionsofselectedworkshoppapers,wehaveincludedinvited contributions from leading experts in the ?eld and closely related areas. With this, the present volume represents the ?rst comprehensive compilation of state-of-the-art research approaches to uncertainty reasoning in the context of the Semantic Web, capturing di erent models of uncertainty and approaches to deductive as well as inductive reasoning with uncertain formal knowledge. TheWorldWide Web communityenvisionse ortless interactionbetween- mansandcomputers,seamlessinteroperabilityandinformationexchangeamong Webapplications,andrapidandaccurateidenti cationandinvocationofapp- priate Web services.As workwith semantics and servicesgrowsmoreambitious, there is increasing appreciation of the need for principled approaches to the f- mal representation of and reasoning under uncertainty. The term uncertainty is intended here to encompass a variety of forms of incomplete knowledge, - cluding incompleteness, inconclusiveness, vagueness, ambiguity, and others. The termuncertaintyreasoning ismeanttodenotethefullrangeofmethodsdesigned for representing and reasoning with knowledge when Boolean truth values are unknown, unknowable, or inapplicable. Commonly applied approachesto unc- tainty reasoning include probability theory, Dempster-Shafer theory, fuzzy logic and possibility theory, and numerous other methodologies. A few Web-relevant challenges which are addressed by reasoning under - certainty include: Uncertaintyofavailableinformation: MuchinformationontheWorldWide Web is uncertain. Examples include weather forecasts or gambling odds. Canonical methods for representing and integrating such information are necessaryforcommunicating it ina seamlessfashion.
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.