Query-Based Entailment and Inseparability for

Authors: ALC

IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical We investigate the problem whether two ALC knowledge bases are indistinguishable by queries over a given vocabulary. We give model-theoretic criteria and prove that this problem is undecidable for conjunctive queries (CQs) but decidable in 2EXPTIME for unions of rooted CQs. This paper makes a first breakthrough into understanding query entailment and inseparability in these cases, with the main results summarized in Figures 1 and 2 (those marked with (?) are from [Botoeva et al., 2014]).
Researcher Affiliation Academia Elena Botoeva,1 Carsten Lutz,2 Vladislav Ryzhikov,1 Frank Wolter,3 Michael Zakharyaschev4 1Faculty of Computer Science, Free University of Bozen-Bolzano 2Fachbereich Informatik, University of Bremen 3Department of Computer Science, University of Liverpool 4Department of Computer Science, Birkbeck, University of London
Pseudocode No The paper describes algorithmic approaches, such as the use of two-way alternating automata on infinite trees (2ATAs), but it does not contain structured pseudocode or algorithm blocks.
Open Source Code No The paper does not provide concrete access to source code for the methodology described. It mentions a 'full version' and a 'technical report' for omitted proofs but no code repository.
Open Datasets No The paper is theoretical and does not involve empirical studies or dataset training.
Dataset Splits No The paper is theoretical and does not involve empirical studies or dataset splitting for validation.
Hardware Specification No The paper is purely theoretical and does not describe any hardware used for computations or experiments.
Software Dependencies No The paper is purely theoretical and does not provide specific ancillary software details with version numbers.
Experiment Setup No The paper is purely theoretical and does not detail any experimental setup, hyperparameters, or training configurations.