Oblivious and Semi-Oblivious Boundedness for Existential Rules

Authors: Pierre Bourhis, Michel Leclère, Marie-Laure Mugnier, Sophie Tison, Federico Ulliana, Lily Gallois

IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical We study the notion of boundedness in the context of positive existential rules, that is, whether there exists an upper bound to the depth of the chase procedure, that is independent from the initial instance. By focussing our attention on the oblivious and the semi-oblivious chase variants, we give a characterization of boundedness in terms of FO-rewritability and chase termination. We show that it is decidable to recognize if a set of rules is bounded for several classes and outline the complexity of the problem. Our main contribution is a characterization of boundedness in terms of chase termination and FO-rewritability.
Researcher Affiliation Academia Pierre Bourhis1,3,4 , Michel Lecl ere2,4 , Marie-Laure Mugnier2,4 , Sophie Tison3,4 , Federico Ulliana2,4 and Lily Galois3,4 1 CNRS, France 2 Univ. Montpellier, LIRMM, France 3 Univ. Lille, CRISt AL, France 4 Inria, France {firstname.lastname}@inria.fr
Pseudocode No The paper does not contain any pseudocode or clearly labeled algorithm blocks.
Open Source Code No The paper does not provide any statements about the availability of open-source code.
Open Datasets No The paper is theoretical and does not conduct experiments involving datasets or training. Therefore, it does not mention a publicly available dataset.
Dataset Splits No The paper is theoretical and does not involve experiments with dataset splits for training, validation, or testing.
Hardware Specification No The paper focuses on theoretical concepts and does not describe computational experiments or specific hardware used.
Software Dependencies No The paper is theoretical and does not mention any software dependencies with version numbers.
Experiment Setup No The paper is theoretical and does not describe an experimental setup with hyperparameters or training configurations.