Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Query-Based Entailment and Inseparability for
Authors: ALC
IJCAI 2016 | Venue PDF | 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 ο¬rst 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. |