From Classical to Consistent Query Answering under Existential Rules

Authors: Thomas Lukasiewicz, Maria Vanina Martinez, Andreas Pieris, Gerardo Simari

AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical The goal of the current work is to perform an in-depth analysis of the complexity of consistent query answering under the main decidable classes of existential rules enriched with negative constraints. Our investigation focuses on one of the most prominent inconsistency-tolerant semantics, namely, the AR semantics. We establish a generic complexity result, which demonstrates the tight connection between classical and consistent query answering. This result allows us to obtain in a uniform way a relatively complete picture of the complexity of our problem.
Researcher Affiliation Academia 1Department of Computer Science, University of Oxford, UK 2Departamento de Ciencias e Ingenier ıa de la Computaci on, Universidad Nacional del Sur and CONICET, Argentina 3Institute of Information Systems, Vienna University of Technology, Austria thomas.lukasiewicz@cs.ox.ac.uk, {mvm,gis}@cs.uns.edu.ar, pieris@dbai.tuwien.ac.at
Pseudocode Yes ALGORITHM 1: The algorithm ARCQAns
Open Source Code No The paper is theoretical and does not mention releasing any source code for its methodology.
Open Datasets No The paper is theoretical and does not conduct experiments with datasets. It discusses 'databases' and 'instances' in a formal, theoretical context, but does not refer to specific publicly available datasets for training.
Dataset Splits No The paper is theoretical and does not involve experiments with dataset splits for validation.
Hardware Specification No The paper is theoretical and does not describe any hardware used for experiments.
Software Dependencies No The paper is theoretical and does not specify any software dependencies with version numbers for experimental reproducibility.
Experiment Setup No The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training configurations.