Query Conservative Extensions in Horn Description Logics with Inverse Roles
Authors: Jean Christoph Jung, Carsten Lutz, Mauricio Martel, Thomas Schneider
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We investigate the decidability and computational complexity of query conservative extensions in Horn description logics (DLs) with inverse roles. This is more challenging than without inverse roles because characterizations in terms of unbounded homomorphisms between universal models fail, blocking the standard approach to establishing decidability. We resort to a combination of automata and mosaic techniques, proving that the problem is 2EXPTIME-complete in Horn-ALCHIF (and also in Horn-ALC and in ELI). |
| Researcher Affiliation | Academia | Jean Christoph Jung, Carsten Lutz, Mauricio Martel, and Thomas Schneider Fachbereich Informatik, Universit at Bremen, Germany |
| Pseudocode | No | The paper describes theoretical methods and derivations but does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code, nor does it explicitly state that code for the described methodology is released. |
| Open Datasets | No | The paper is theoretical and does not involve datasets, training, or public data access. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental procedures involving dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not involve computational experiments, thus no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not involve computational experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not involve experiments, therefore no experimental setup details like hyperparameters or training configurations are provided. |