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..
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. |