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 [1].
Lightweight Temporal Description Logics with Rigid Roles and Restricted TBoxes
Authors: Víctor Gutiérrez-Basulto, Jean Christoph Jung, Thomas Schneider
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study temporal description logics (TDLs) based on the branching-time temporal logic CTL and the lightweight DL EL in the presence of rigid roles and restricted TBoxes. ... As our main contribution, we identify several TDLs of elementary complexity, obtained by combining EL with CTL fragments that allow only restricted sets of temporal operators. We obtain upper complexity bounds ranging from PTIME to CONEXPTIME and mostly tight lower bounds. |
| Researcher Affiliation | Academia | V ıctor Guti errez-Basulto and Jean Christoph Jung and Thomas Schneider Universit at Bremen, Germany EMAIL |
| Pseudocode | Yes | Algorithm 1: Subsumption in CTLE3,A2 EL |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. It only provides a link to a report for 'additional technical notions and proofs'. |
| Open Datasets | No | The paper is theoretical and does not use or refer to any datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve data splitting for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific ancillary software details with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not provide details on experimental setup, hyperparameters, or system-level training settings. |