Scalable Uncertainty Management [electronic resource] : 8th International Conference, SUM 2014, Oxford, UK, September 15-17, 2014. Proceedings / edited by Umberto Straccia, Andrea Calì.

Possibilistic networks: A new setting for modeling preferences -- Min-based assertional merging approach for possibilistic DL-Lite knowledge bases -- On the revision of possibilistic DL-Lite knowledge bases -- Interval-based possibilistic networks -- Tractable vs. Intractable Cases of Query Answering under Matching Dependencies -- Analogical classification: Handling numerical data -- Lazy Analogical Classification: Optimization and Precision Issues -- Update Operators for Inconsistent Query Answering: A New Point of View -- Improving Inconsistency Resolution by Considering Global Conflicts -- Probabilistic Argumentation Frameworks - A Logical Approach -- Computing Skyline from Evidential Data -- A Two-Level Approach to Maximum Entropy Model Computation for Relational Probabilistic Logic Based on Weighted Conditional Impacts: Solving Hidden-Semi-Markov-Mode Markov Decision Problems -- Probabilistic Strategies in Dialogical Argumentation -- Analytics over Probabilistic Unmerged Duplicates -- Answering Ontological Ranking Queries based on Subjective Reports -- A Petri net model of argumentation dynamics -- Integrity Constraints for Probabilistic Spatio-Temporal Knowledgebases.

This book constitutes the refereed proceedings of the 8th International Conference on Scalable Uncertainty Management, SUM 2014, held in Oxford, UK, in September 2014. The 20 revised full papers and 6 revised short papers were carefully reviewed and selected from 47 submissions. The papers cover topics in all areas of managing and reasoning with substantial and complex kinds of uncertain, incomplete or inconsistent information including applications in decision support systems, machine learning, negotiation technologies, semantic web applications, search engines, ontology systems, information retrieval, natural language processing, information extraction, image recognition, vision systems, data and text mining, and the consideration of issues such as provenance, trust, heterogeneity, and complexity of data and knowledge.