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].
Non-Objection Inference for Inconsistency-Tolerant Query Answering
Authors: Salem Benferhat, Zied Bouraoui, Madalina Croitoru, Odile Papini, Karim Tabia
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also give experimental results of the proposed non-objection inference.For experimental evaluation, we implemented a tool that checks whether a query q is a no-consequence of a DL-Lite R KB K. As benchmark (available at https://code.google.com/p/combo-obda/). |
| Researcher Affiliation | Academia | Salem Benferhat Univ Artois, France EMAIL Zied Bouraoui Aix-Marseille Univ, France EMAIL Madalina Croitoru Univ Montpellier, France EMAIL Odile Papini Aix-Marseille Univ, France EMAIL Karim Tabia Univ Artois, France EMAIL |
| Pseudocode | No | The paper does not contain pseudocode or a clearly labeled algorithm block. |
| Open Source Code | No | The paper refers to a benchmark (LUBM920 ontology) available at 'https://code.google.com/p/combo-obda/', but this link is for the benchmark itself, not the source code for the methodology described in the paper. |
| Open Datasets | Yes | As benchmark (available at https://code.google.com/p/combo-obda/), we considered the LUBM920 ontology (i.e. TBox), which corresponds to the DL-Lite R version of the original LUBM ontology [Lutz et al., 2013], and we used the Extended University Data Generator (EUDG) in order to generate the ABox assertions. |
| Dataset Splits | No | The paper evaluates its methods on different ABoxes and queries but does not describe training, validation, or test dataset splits typically used for model reproduction or evaluation. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments. |
| Software Dependencies | No | The paper mentions evaluating over an ABox stored as a DB using a 'SQLite engine', but it does not specify a version number for SQLite or any other software dependencies. |
| Experiment Setup | No | The paper describes the benchmark and data generation process, and reports execution times for various operations. However, it does not provide specific details about hyperparameters, model initialization, or system-level training settings as would be found in a typical experimental setup description for a machine learning paper. |