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..
Conservative Rewritability of Description Logic TBoxes
Authors: Boris Konev, Carsten Lutz, Frank Wolter, Michael Zakharyaschev
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We investigate the problem of conservative rewritability of a TBox T in a description logic (DL) L into a TBox T 0 in a weaker DL L0. We focus on model-conservative rewritability (T 0 entails T and all models of T are expandable to models of T 0), subsumption-conservative rewritability (T 0 entails T and all subsumptions in the signature of T entailed by T 0 are entailed by T ), and standard DLs between ALC and ALCQI. We give model-theoretic characterizations of conservative rewritability via bisimulations, inverse p-morphisms and generated subinterpretations, and use them to obtain a few rewriting algorithms and complexity results for deciding rewritability. |
| Researcher Affiliation | Academia | 1Univ. of Liverpool, UK 2Univ. of Bremen, Germany 3Birkbeck, Univ. of London, UK EMAIL EMAIL EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. It focuses on theoretical characterizations and proofs. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not involve experimental evaluation on datasets. Thus, no dataset information, public or otherwise, is provided for training or other purposes. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with datasets. Therefore, no training/validation/test splits are mentioned. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for computations or experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers, as it focuses on theoretical results and not practical implementations or experiments. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details, hyperparameters, or system-level training settings. |