http://dl.gi.de/handle/20.500.12116/11026 2026-07-21T13:13:57Z http://dl.gi.de/handle/20.500.12116/11043 Polynomial Algorithms for Computing a Single Preferred Assertional-Based Repair Telli, Abdelmoutia; Benferhat, Salem; Bourahla, Mustapha; Bouraoui, Zied; Tabia, Karim This paper investigates different approaches for handling inconsistent DL-Lite knowledge bases in the case where the assertional base is prioritized and inconsistent with the terminological base. The inconsistency problem often happens when the assertions are provided by multiple conflicting sources having different reliability levels. We propose different inference strategies based on the selection of one consistent assertional base, called a preferred repair. For each strategy, a polynomial algorithm for computing the associated single preferred repair is proposed. Selecting a unique repair is important since it allows an efficient handling of queries. We provide experimental studies showing (from a computational point of view) the benefits of selecting one repair when reasoning under inconsistency in lightweight knowledge bases. 2017-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/11044 A Practical Comparison of Qualitative Inferences with Preferred Ranking Models Beierle, Christoph; Eichhorn, Christian; Kutsch, Steven When reasoning qualitatively from a conditional knowledge base, two established approaches are system Z and p-entailment. The latter infers skeptically over all ranking models of the knowledge base, while system Z uses the unique pareto-minimal ranking model for the inference relations. Between these two extremes of using all or just one ranking model, the approach of c-representations generates a subset of all ranking models with certain constraints. Recent work shows that skeptical inference over all c-representations of a knowledge base includes and extends p-entailment. In this paper, we follow the idea of using preferred models of the knowledge base instead of the set of all models as a base for the inference relation. We employ different minimality constraints for c-representations and demonstrate inference relations from sets of preferred c-representations with respect to these constraints. We present a practical tool for automatic c-inference that is based on a high-level, declarative constraint-logic programming approach. Using our implementation, we illustrate that different minimality constraints lead to inference relations that differ mutually as well as from system Z and p-entailment. 2017-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/11042 Special Issue on Challenges for Reasoning under Uncertainty, Inconsistency, Vagueness, and Preferences Kern-Isberner, Gabriele; Lukasiewicz, Thomas 2017-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/11041 DFG Research Unit (Forschergruppe) FOR 1513 Hybrid Reasoning for Intelligent Systems Lakemeyer, Gerhard 2017-01-01T00:00:00Z