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..
Inconsistency-Tolerant Ontology-Based Data Access Revisited: Taking Mappings into Account
Authors: Meghyn Bienvenu
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | After formalizing the problem, we perform a detailed analysis of the data complexity of inconsistency-tolerant OBDA for ontologies formulated in DL-Lite and other data-tractable description logics, considering three different semantics (AR, IAR, and brave), two notions of repairs (subset and symmetric difference), and two classes of global-as-view (GAV) mappings. We show that adding plain GAV mappings does not affect data complexity, but there is a jump in complexity if mappings with negated atoms are considered. |
| Researcher Affiliation | Academia | Meghyn Bienvenu French National Center for Scienti๏ฌc Research (CNRS) University of Montpellier French Institute for Research in Computer Science and Automation (Inria) |
| Pseudocode | No | The paper describes theoretical concepts, definitions, and theorems, but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper discusses existing OBDA systems and mapping languages (e.g., Ontop, R2RML) but does not provide a statement or link for open-sourcing the code for the work presented in this paper. |
| Open Datasets | No | The paper is a theoretical work focusing on complexity analysis and formal definitions. It does not use or refer to specific datasets for training or evaluation. |
| Dataset Splits | No | The paper is a theoretical work and does not involve empirical validation with dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe experiments that would require specific hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is a theoretical study and does not include details about an experimental setup, hyperparameters, or training configurations. |