Scalable Uncertainty Management

This volume contains the papers presented at the Third International Conference on Scalable Uncertainty Management, SUM 2009, in Washington, DC, September 28-30, 2009. It contains 21 technical papers which were selected out of 30 submitted papers in a rigourous reviewing process. The volume also contains extended abstracts of two invited talks. The volume reflects the growing interest in uncertainty and incosistency and aims at bringing together all those interested in the management of uncertainty and inconsistency at large.

Inhalt
PrDB: Managing Large-Scale Correlated Probabilistic Databases (Abstract).- Uncertainty in the Semantic Web.- Bridging the Gap between Abstract Argumentation Systems and Logic.- Modeling Unreliable Observations in Bayesian Networks by Credal Networks.- Interventions in Possibilistic Logic.- An Analysis of Sum-Based Incommensurable Belief Base Merging.- An Argument-Based Approach to Using Multiple Ontologies.- A Model Based on Possibilistic Certainty Levels for Incomplete Databases.- A Proposal for Making Argumentation Computationally Capable of Handling Large Repositories of Uncertain Data.- Making Sense of a Sequence of Events: A Psychologically Supported AI Implementation.- Explaining Inconsistencies in OWL Ontologies.- On Improving the Scalability of Checking Satisfiability in Probabilistic Description Logics.- Towards Relational Schema Uncertainty.- Aggregation of Trust for Iterated Belief Revision in Probabilistic Logics.- Fast and Accurate Prediction of the Destination of Moving Objects.- Weighted Description Logics Preference Formulas for Multiattribute Negotiation.- Probabilistic Planning with Imperfect Sensing Actions Using Hybrid Probabilistic Logic Programs.- Extended Fuzzy Logic Programs with Fuzzy Answer Set Semantics.- Finite Satisfiability in Infinite-Valued ?ukasiewicz Logic.- Compression of Probabilistic XML Documents.- Query Answering in Belief Logic Programming.- Towards Effective Elicitation of NIN-AND Tree Causal Models.- An Evidence-Theoretic k-Nearest Neighbor Rule for Multi-label Classification.