Scalable Uncertainty Management

Managing uncertainty and inconsistency has been extensively explored in - ti?cial Intelligence over a number of years. Now with the advent of massive amounts of data and knowledge from distributed heterogeneous,and potentially con?icting, sources, there is interest in developing and applying formalisms for uncertainty andinconsistency widelyin systems that need to better managethis data and knowledge. The annual International Conference on Scalable Uncertainty Management (SUM) has grown out of this wide-ranging interest in managing uncertainty and inconsistency in databases, the Web, the Semantic Web, and AI. It aims at bringing together all those interested in the management of large volumes of uncertainty and inconsistency, irrespective of whether they are in databases,the Web, the Semantic Web, or in AI, as well as in other areas such as information retrieval, risk analysis, and computer vision, where signi?cant computational - forts are needed. After a promising First International Conference on Scalable Uncertainty Management was held in Washington DC, USA in 2007, the c- ference series has been successfully held in Napoli, Italy, in 2008, and again in Washington DC, USA, in 2009.

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Klappentext

This book constitutes the refereed proceedings of the 4th International Conference on Scalable Uncertainty Management, SUM 2010, held in Toulouse, France, in September 2010. The 26 revised full papers presented together with the abstracts of 2 invited talks and 6 "discussant" contributions were carefully reviewed and selected from 32 submissions. The papers cover all areas of managing substantial and complex kinds of uncertainty and inconsistency in data and knowledge, including applications in decision-support systems, negotiation technologies, semantic web applications, search engines, ontology systems, information retrieval, natural language processing, information extraction, image recognition, vision systems, text mining, and data mining, and consideration of issues such as provenance, trust, heterogeneity, and complexity of data and knowledge.

Inhalt
Invited Talks.- Markov Chain Monte Carlo and Databases.- Answer Set Programming, the Solving Paradigm for Knowledge Representation and Reasoning.- Discussant Contributions.- Graphical and Logical-Based Representations of Uncertain Information in a Possibility Theory Framework.- Probabilistic Data: A Tiny Survey.- The Role of Epistemic Uncertainty in Risk Analysis.- Uncertainty in Clustering and Classification.- Information Fusion.- Use of the Domination Property for Interval Valued Digital Signal Processing.- Regular Contributions.- Managing Lineage and Uncertainty under a Data Exchange Setting.- A Formal Analysis of Logic-Based Argumentation Systems.- Handling Inconsistency with Preference-Based Argumentation.- A Possibility Theory-Oriented Discussion of Conceptual Pattern Structures.- DK-BKM: Decremental K Belief K-Modes Method.- On the Use of Fuzzy Cardinalities for Reducing Plethoric Answers to Fuzzy Queries.- From Bayesian Classifiers to Possibilistic Classifiers for Numerical Data.- Plausibility of Information Reported by Successive Sources.- Combining Semantic Web Search with the Power of Inductive Reasoning.- Evaluating Trust from Past Assessments with Imprecise Probabilities: Comparing Two Approaches.- Range-Consistent Answers of Aggregate Queries under Aggregate Constraints.- Characterization, Propagation and Analysis of Aleatory and Epistemic Uncertainty in the 2008 Performance Assessment for the Proposed Repository for High-Level Radioactive Waste at Yucca Mountain, Nevada.- Comparing Evidential Graphical Models for Imprecise Reliability.- Imprecise Bipolar Belief Measures Based on Partial Knowledge from Agent Dialogues.- Kriging with Ill-Known Variogram and Data.- Event Modelling and Reasoning with Uncertain Information for Distributed Sensor Networks.- Uncertainty in Decision Tree Classifiers.- Efficient Policy-Based Inconsistency Management in Relational Knowledge Bases.- Modelling Probabilistic Inference Networks and Classification in Probabilistic Datalog.- Handling Dirty Databases: From User Warning to Data Cleaning Towards an Interactive Approach.- Disjunctive Fuzzy Logic Programs with Fuzzy Answer Set Semantics.- Cost-Based Query Answering in Action Probabilistic Logic Programs.- Clustering Fuzzy Data Using the Fuzzy EM Algorithm.- Combining Multi-resolution Evidence for Georeferencing Flickr Images.- A Structure-Based Similarity Spreading Approach for Ontology Matching.- Risk Modeling for Decision Support.