Inconsistency Measures for Repair Semantics in OBDA
Authors: Bruno Yun, Srdjan Vesic, Madalina Croitoru, Pierre Bisquert
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also provide an implementation of our approach and discuss its performance. Furthermore, we show the significance and the practical interest of our approach using the real data collected in the framework of the Pack4Fresh project for reducing food wastes. We ran the algorithm on the KBs described by [Yun et al., 2017] and compared its performance with a basic algorithm... All experiments were performed on a Mac machine running on mac OS High Sierra with an Intel core i5 2.8 GHz and 8GB of RAM and were reproduced multiple times. |
| Researcher Affiliation | Academia | 1 Univ. Montpellier, LIRMM 2 CRIL CNRS, Univ. Artois 3 INRA, LIRMM |
| Pseudocode | Yes | Algorithm 1: The OFRecc algorithm |
| Open Source Code | Yes | The KB in DLGP format as well as a JAVA implementation of the tool for computing the output of our framework is accessible at: https://gite.lirmm.fr/yun/IJCAI2018. |
| Open Datasets | Yes | using the real data collected in the framework of the Pack4Fresh project... The KB in DLGP format as well as a JAVA implementation of the tool for computing the output of our framework is accessible at: https://gite.lirmm.fr/yun/IJCAI2018. |
| Dataset Splits | No | The paper mentions KBs were split into two sets (A and B) for performance evaluation, but it does not specify traditional training, validation, and test splits for a model, nor does it provide proportions or sample counts for such splits. The evaluation is on the algorithm's performance, not a trained model. |
| Hardware Specification | Yes | All experiments were performed on a Mac machine running on mac OS High Sierra with an Intel core i5 2.8 GHz and 8GB of RAM and were reproduced multiple times. |
| Software Dependencies | No | The paper mentions a "JAVA implementation of the tool" but does not specify a version number for Java or any other software dependencies. |
| Experiment Setup | No | The paper describes the characteristics of the KBs used for testing (number of facts, rules, constraints) and the application scenario's data formalization. However, it does not provide specific hyperparameter values, optimizer settings, or other system-level training configurations typically found in experimental setups for machine learning models or algorithms with configurable parameters. |