- ホーム
- > 洋書
- > 英文書
- > Computer / General
Full Description
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.
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.
