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10:15-10:45Coffee Break
10:45-12:15 Session 183G: Ontology-based reasoning, probably
Location: FH, Seminarraum 107
Answering Ontological Ranking Queries Based on Subjective Reports
SPEAKER: unknown

ABSTRACT. The use of preferences in query answering, both in traditional databases and in ontology-based data access, has recently received much attention, due to its many real-world applications. In this paper, we tackle the problem of query answering in Datalog+/– ontologies subject to the querying user’s preferences and a collection of subjective reports (i.e., scores for a list of features) of other users, who have their own preferences as well. All these pieces of information are combined to rank the query results. We first focus on the problem of ranking atoms in a database by leveraging reports and customizing their content according to the user’s preferences. Then, we extend this approach to deal with ontological query answering using provenance information. Though the general problem is shown to have an exponential-time data complexity upper bound, we propose a special case that has polynomial time data complexity.

A New DL‐Lite N Bool Probabilistic Extension Using Belief
SPEAKER: Ala Djeddai

ABSTRACT. Dealing with uncertainty is a very important issue in description logics (DLs). In this paper, we present PrDL-Lite N bool a new probabilistic extension of DL-Lite N bool by supporting the belief interval in a single axiom or a set of axioms connected with conjunction (by ∧) or disjunction (by ∨) operators. The PrDL-Lite N bool semantics is based on DL-Lite N bool features which are a new alternative semantics for DL-Lite N bool having a finite structure and the number of them is always finite unlike classical models. PrDL-Lite N bool supports terminological and assertional probabilistic knowledge and the reasoning  tasks: satisfiability, deciding the probabilistic axiom entailment and computing tight interval for entailment are achieved by solving linear constraints system. A prototype of the approach is implemented using OWLAPI for knowledge base creation, Pellet for reasoning and LpSolve for solving the linear programs.

Generation of Parametrically Uniform Knowledge Bases in a Relational Probabilistic Logic with Maximum Entropy Semantics

ABSTRACT. In a relational setting, the maximum entropy model of a set of probabilistic conditionals can be defined referring to the full set of ground instances of the conditionals. The logic FO-PCL uses the notion of parametric uniformity to ensure that the full grounding of the conditionals can be avoided, thereby greatly simplifying the maximum entropy model computation. In this paper, we describe a system that realises an approach transforming an FO-PCL knowledge base consisting of relational probabilistic conditionals into a knowledge base having the same maximum entropy model that is parametrically uniform. The implemented system provides different execution and evaluation modes, including the generation of all possible solutions, and is available within an integrated development environment for relational probabilistic logic.

13:00-14:30Lunch Break
16:00-16:30Coffee Break